Search Results for author: Partha Pratim Das

Found 13 papers, 4 papers with code

Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023

no code implementations27 Oct 2023 Srijoni Majumdar, Soumen Paul, Debjyoti Paul, Ayan Bandyopadhyay, Samiran Chattopadhyay, Partha Pratim Das, Paul D Clough, Prasenjit Majumder

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels.

Binary Classification Information Retrieval +3

Smart Knowledge Transfer using Google-like Search

no code implementations12 Aug 2023 Srijoni Majumdar, Partha Pratim Das

To address the issue of rising software maintenance cost due to program comprehension challenges, we propose SMARTKT (Smart Knowledge Transfer), a search framework, which extracts and integrates knowledge related to various aspects of an application in form of a semantic graph.

Transfer Learning

Improving Contextualized Topic Models with Negative Sampling

2 code implementations27 Mar 2023 Suman Adhya, Avishek Lahiri, Debarshi Kumar Sanyal, Partha Pratim Das

Topic modeling has emerged as a dominant method for exploring large document collections.

Topic Models

Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings

1 code implementation14 Feb 2023 Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay, Plaban Kumar Bhowmick, Partha Pratim Das

On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31. 78, 9. 76 and 29. 3, respectively, METEOR score of 24. 00, and BERTScore F1 of 85. 25.

Incorporating Domain Knowledge To Improve Topic Segmentation Of Long MOOC Lecture Videos

no code implementations8 Dec 2020 Ananda Das, Partha Pratim Das

This segmentation information eases user efforts of searching, locating and browsing a topic inside a lecture video.

Language Modelling Segmentation

Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach

no code implementations24 Sep 2019 Tanwi Mallick, Partha Pratim Das, Arun Kumar Majumdar

To develop an application for dance, three aspects of dance analysis need to be addressed: 1) Segmentation of the dance video to find the representative action elements, 2) Matching or recognition of the detected action elements, and 3) Recognition of the dance sequences formed by combining a number of action elements under certain rules.

BIG-bench Machine Learning Recommendation Systems

HSD-CNN: Hierarchically self decomposing CNN architecture using class specific filter sensitivity analysis

no code implementations11 Nov 2018 K. Sai Ram, Jayanta Mukherjee, Amit Patra, Partha Pratim Das

We report accuracies up to $85. 6\%$ ( $94. 75\%$ ) on scenarios with 13 ( 4 ) classes of CIFAR100, using a pre-trained VGG-16 network on the full data set.

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