Category Archives: Project

WSL project

The Cogno – Web Observatory: Characterize Online Social Cognition

It is important to occasionally remember that the World Wide Web (WWW) is the largest information network the world has ever seen. Just about every sphere of human activity has been altered in some way, due to the web. Our understanding of the web has been evolving over the past few decades ever since it was born. In its early days, the web was
seen just as an unstructured hypertext document collection. However, over time, we have come to model the web as a global, participatory, socio-cognitive space. One of the consequences of modeling the web as a space rather than as a tool, is the emergence of the concept of Web observatories. These are application programs that are meant to observe and curate data about online phenomena. This paper details the design of a Web observatory called Cogno, that is meant to observe online social cognition. Social cognition refers to the way social discourses lead to the formation of collective worldviews. As part of the design of Cogno, we also propose a computational model for characterizing social cognition. Social media is modeled as a “marketplace of opinions” where different opinions come together to form “narratives” that not only drive the discourse, but may also bring some form of returns to the opinion holders. The problem of characterizing social cognition is defined as breaking down a social discourse into its constituent narratives, and for each narrative, its key opinions, and the key people driving the narrative.

  • Demonstration:
  • Current Project Members
  • Previous Project Members
  • Publications
    • Raksha Pavagada Subbanarasimha. 2019. Designing the Cogno-Web Observatory: To Characterize the Dynamics of Online Social Cognition. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM ’19). ACM, New York, NY, USA, 814-815. DOI: https://doi.org/10.1145/3289600.3291600.
    • Srinivasa S., Pavagada Subbanarasimha R. (2018) Design of the Cogno Web Observatory for Characterizing Online Social Cognition. In: Anirban Mondal, Himanshu Gupta, Jaideep Srivastava, P.Krishna Reddy, D.V.L.N. Somayajulu. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science. Springer, Cham. (To appear)
    • Raksha Pavagada Subbanarasimha, Lokesh Todwal, Mamillapalli Rachana, Aditya Naidu, and Srinath Srinivasa. 2018. Mithya: A Framework For Identifying Opinion Drivers On Social Media. Demo at ACM IKDD Conference on Data Science and International Conference on Management of Data, Goa, India, Jan 2018 (CODS-COMAD 2018).
    • Anish Bhanushali, Raksha Pavagada Subbanarasimha, and Srinath Srinivasa. 2017. Identifying Opinion Drivers on Social Media. In On the Move to Meaningful Internet Systems. OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part II. Springer International Publishing, Cham, 242–253.

Gooru Algorithms (Deep-end tables)

This project is about implementation and validation of Gooru algorithms in the deep-end table. Deep end engineering is implemented in the form of a collection of “big tables” that contain materialized data relevant to several semantic operations. There are two big tables that come under deep-end big tables.

1) Activity Big Table: This table characterizes different elements of learning resources. These elements or columns include: Transcript, Summarization, Phrase Cloud, Competency, Relevance, Engagement, Efficacy, Instructional Practice and Rigor. This is part of curation of learning resources by using big data analytics.

It includes implementation of classification algorithm, relevance, engagement and efficacy scores. After computation of the scores for a subset of resources in datascope db, these scores will be computed for all the classified resources (3.4 million) from nucleaus db and will be populated for all the classified resources in the ABT of deep-end. Each of these columns are computed for a pair of learning resource and a competency.

2) Learning Big Table: This table characterizes a learner’s learning profile with respect to a subject. These elements or columns include performance, progress, proficiency, citizenship, authority, markers and goals.This is part of location of learner by using big data analytics.

It includes implementation of algorithms for computation of performance, progress, proficiency, authority and citizenship for learners and populate it into the LBT of deep-end.

Columns such as competency, transcripts, summarization, relevance, engagement, performance, progress, proficiency, authority and citizenship are implemented.

Implementation for computation of remaining columns is in progress.

Project Members:

1) Dr Aparna Lalingkar
2) Chaitali Diwan
3) Praseeda K
4) Udit Jindal
5) Ankit Gahalawat
6) Prasun Joshi
7) Palash Gupta
8) Urja Kothari
9) Rachna Shriwas
10) Shreyans Vora
11) Arjun Singh Kanwal

Tweet Summarization

The project involves extracting and collating important information from large volume of  short reports.

  •  Characterization of important entities and actions
  • Mine and associate semantics into entities and actions
  • Semi-automate the summary generation process by generating a set of candidate sentences
  • Based on key entities and key actions of interest
  •  User feedback to refine the sentences

Skyline

Gooru is a learning environment modeled as a social machine. It acts as a learning navigator. Any learning experience is in the form of a navigation across the “learning map” using “competencies”. Competencies are made up of two dimensions; namely pedagogic depth and topic. In order to learn a particular topic or to reach a target competency, Gooru provides the learner with an optimal learning route. The skyline of a learner is the set of the highest achieved competencies in every topic. Every time the learner learns a new topic or achieves a new competency, his skyline is updated. This gives the learner his position on the learning map. The learner skyline is used to compute the route the learner has to take to reach a target competency.

  • Project Members

Machine Classification of Learning Resource

Every competency level is associated with many learning resources manually assigned by teachers. In this project, we have developed a classifier to automatically map a learning resource to set of competencies. This helps in providing similar resources to students in a particular lesson.

  • Project Members
    • Ashish Kumar Pani
    • Prajith Kumar Chilummula
    • Shankul Jain

Gooru

Gooru honors the human right to education by creating technology that enables educators and researchers to “open-source” effective practices and content to improve learning outcomes for all.

The Gooru model of learning is a Computer Aided Instruction (CAI) environment that aims to provide effective, individualized learning experiences at scale, by utilizing both adaptive tutoring as well as community interactions. User interaction is modeled around a “Navigation” paradigm, where the subject matter is represented as learning activities scattered across a logical space called the “learning map.” Lessons are modeled as learning pathways in this logical space. The logical space metaphor enables the student to obtain different levels of overview of the subject matter by perusing the landscape, and understand semantic relatedness of topics based on their visual proximity. The learning map is also a social space, where students interact with other learners (other students, human tutors and teachers), rather than only an algorithmic agent.

The underlying paradigm of such a system is called a social machine. The social machine approach is characteristically different from both scalable classroom models like MOOCs and personalized learning environments like Intelligent Tutoring Systems

Sub Projects

Narrative Arc

This project aims to compute Narrative Arc for the learning activities in Gooru. The Narrative Arc refers to the semantic structure of a sequence of learning activities suggested to the user for learning. The suggested sequence of learning activities that are presented to the user need to result in a coherent narrative and it should be engaging to the user for a greater learning experience. A Narrative Arc hence comprises of two elements: Semantic coherence and Cognitive engagement.

  • Current Project Members
  • Previous Project Members
    • Sanket Kutumbe
    • Karan Kumar Gupta
    • Srinivasan P.S
  • Publications
    • 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.

Co-creation of a Center of Excellence in Big Data Engineering

This project aims to establish a collaboration between the International Institute of Information Technology (IIIT-B), with City University London, as the UK partner and Siemens Research, India, as the industry partner, to set up a centre of excellence in Big Data Engineering. With emerging trends like Web Science and the Internet of Things, expertise in Big Data is going to be in high demand in the future. As part of our initiatives to create a talent pool of research and engineering expertise, IIIT-B has collaborated with several partners in this area on specific projects. This project aims to consolidate our disparate activities in this area and create a Centre of Excellence in Big Data Engineering. The term “Big Data” is defined here to mean any kind of data management problem for which, conventional RDBMS based solutions are inadequate. The “Big” refers to not just the volume of data, but also challenges concerning variety, veracity and velocity of the data.

Collaborators:

  • Dr. Srinath Srinivasa IIIT Bangalore
  • Prof Muttukrishnan Rajarajan, City University, Northampton Square , London,
    United Kingdom
  • Dr. Amarnath Bose, Siemens Technology and Service, Bangalore
  • Praseeda , Research Scholar, IIIT Bangalore
  • Raksha, Research Scholar, IIIT Bangalore
  • Anish, Mtech. Thesis Student, IIIT Bangalore

Events

  • A two day workshop on Big Data on April 18th and 19th at IIIT Bangalore

Associated Projects

Demonstration:

Reports

Publications:

  • Srinivasa S., Pavagada Subbanarasimha R. (2018) Design of the Cogno Web Observatory for Characterizing Online Social Cognition. In: Anirban Mondal, Himanshu Gupta, Jaideep Srivastava, P.Krishna Reddy, D.V.L.N. Somayajulu. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science. Springer, Cham. (To appear)
  • Raksha Pavagada Subbanarasimha, Lokesh Todwal, Mamillapalli Rachana, Aditya Naidu, and Srinath Srinivasa. 2018. Mithya: A Framework For Identifying Opinion Drivers On Social Media. Demo at ACM IKDD Conference on Data Science and International Conference on Management of Data, Goa, India, Jan 2018 (CODS-COMAD 2018).
  • Anish Bhanushali, Raksha Pavagada Subbanarasimha, and Srinath Srinivasa. 2017. Identifying Opinion Drivers on Social Media. In On the Move to Meaningful Internet Systems. OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part II. Springer International Publishing, Cham, 242–253.

SANDESH

 

Sandesh is Semantic Data Mesh for publishing of Knowledge aggregated from Indian Open Data. Open structured data is published by several agencies like World Health Organization (WHO), United Nations Organization (UNO), private firms, NGOs, governmental bodies etc. Government of India publishes open data on its data portal called data.gov.in. To aggregate and integrate data from disparate datasets,  a framework called Many Worlds on a Frame (MWF) is proposed. The framework is partially implemented in software called RootSet on top of which, the module Sandesh is implemented.