Bhaskarjyoti Das
@pes.edu
Adjunct Professor , Department of Computer Science and Engineering in AI and ML
PES University
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Bhaskarjyoti Das, Krithika Ragothaman, Raghav T. Kesari, and Sudarshan T.S.B.
Elsevier BV
Atharv Tiwari, Shreyash Chatterjee, Siddharth Padmakumar, Sushanth Nair, and Bhaskarjyoti Das
Springer Science and Business Media LLC
Kaveesh Khattar and Bhaskarjyoti Das
Springer Nature Singapore
Bhaskarjyoti Das and Ammu Mary Laji
Springer Nature Singapore
Sathwik Acharya, Bhaskarjyoti Das, and T. S. B. Sudarshan
Institute of Electrical and Electronics Engineers (IEEE)
Metaphorical memes, where a source concept is projected into a target concept, are an essential construct in figurative language. In this article, we present a novel approach for downstream learning tasks on metaphorical multimodal memes. Our proposed framework replaces traditional methods using metaphor annotations with a metaphor-capturing mechanism. Besides using the significant zero-shot learning capability of state-of-the-art pretrained encoders, this work introduces an alternative external knowledge enhancement strategy based on ChatGPT (chatbot generative pretrained transformer), demonstrating its effectiveness in bridging the intermodal semantic gap. We propose a new concept projection process consisting of three distinct components to capture the intramodal knowledge and intermodal concept gap in the forms of text modality embedding, visual modality embedding, and concept projection embedding. This approach leverages the attention mechanism of the Graph Attention Network for fusing the common aspects of external knowledge related to the knowledge in the text and image modality to implement the concept projection process. Our experimental results demonstrate the superiority of our proposed approach compared to existing methods.
Bhaskarjyoti Das and Sudarshan TSB
Elsevier BV
Kruthika Suresh, Mayuri D Patil, Shrikar Madhu, Yousha Mahamuni, and Bhaskarjyoti Das
ACM
With the advent of social media and technology, the increased connections between individuals and organizations have led to a similar increase in the number of conversations. These conversations, in most cases are bimodal in nature, consisting of both images and text. Existing work in multimodal conversation typically focuses on individual utterances rather than the overall dialogue. The aspect of conversational health is important in many real world conversational uses cases including the emerging world of Metaverse. The work described in this paper investigates conversational health from the viewpoint of emotional concordance in bimodal conversations modelled as graphs. Using this framework, an existing multimodal dialogue dataset has been reformatted as a graph dataset that is labelled with the emotional concordance score. In this work, determination of conversational health has been framed as a graph classification problem. A graph neural network based model using algorithms such as Graph Convolution Network and Graph Attention Network is then used to detect the emotional concordance or discordance based upon the multimodal conversation that is provided. The model proposed in this paper achieves an overall F1 Score of 0.71 for equally sized class training and testing size, which offers improved results compared to previous models using the same benchmark dataset.
Bhaskarjyoti Das, Kruthika Suresh, Shrikar Madhu, Smriti Tila, and Yousha Mahamuni
IEEE
This paper presents a comprehensive analysis of the Facebook Page-Page Large dataset to investigate mutual followership and community segregation. The study leverages the Louvain Algorithm for community detection within subgraphs, utilizing node labeling for subdivision.Moreover, multiclass node-level classification is performed using the Node2Vec technique. Additionally, to predict future mutual followership, GraphML is employed through two state-of-the-art methods: Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).A significant contribution of this paper lies in the unique analysis of communities within subgraphs, deviating from the traditional whole-graph analysis. This approach offers a more targeted examination of community dynamics, augmenting the development of a community-based recommendation system. The findings of this research shed light on the intricate relationships of mutual followership and the presence of distinct communities within the Facebook Page-Page Large dataset. By applying advanced algorithms such as the Louvain Algorithm, Node2Vec, GCN, and GAT, a comprehensive understanding of the dataset's structural patterns and community characteristics is achieved. The results highlight the potential for leveraging subgraph-based community analysis to enhance recommendation systems. The insights gained from this study lay the groundwork for future advancements in community detection and community-aware recommendation algorithms on social media platforms.
Vishal Bharadwaj, Aravind S. Mysore, Ninad Sangli, Shraddha Bharadwaj, and Bhaskarjyoti Das
Springer Nature Singapore
Bhaskarjyoti Das, Shrikar Madhu, Yousha Mahamuni, and Kruthika Suresh
Springer Nature Singapore
Aravind Subramanya Mysore, Vishal Bharadwaj, Rithik R. Mali, and Bhaskarjyoti Das
Springer Nature Singapore
Hariharasudan Savithri Anbarasu, Harshavardhan Veeranna Navalli, Harshita Vidapanakal, K. Manish Gowd, and Bhaskarjyoti Das
Springer Nature Switzerland
Ananya Uppal, P. Maitreyi, P. Shreya, Trisha Jain, and Bhaskaryoti Das
Springer Nature Switzerland
Adithya MS, Mohsin Ahmed, Mihir Madhusudan Kestur, A Sai Chaithanya, and Bhaskarjyothi Das
IEEE
Question-answering (QA) systems are important tools for extracting information from large datasets and providing accurate and relevant answers to user queries. Two of the most widely studied and built QA systems are Natural Language Question Answering (NLQA) and Knowledge Graph Question Answering (KGQA). NLQA relies on sequence learning algorithms, which have limitations on the length of input they can handle, while KGQA relies on the Subject-Predicate-Object (SPO) tuple representation of data, which may not always be available in the knowledge graph. In this paper, we present a novel approach for addressing these challenges by utilizing the structural information from the Knowledge Graph (KG) and the semantic information from the Natural Language Context. Due to the lack of a dataset to enable this approach, we propose the creation of a multi-view dataset - MTL-QA, specifically designed for multi-task learning. We also present a multi-task learning approach to jointly train NLQA and KGQA models and demonstrate the effectiveness on the proposed MTL-QA dataset.
Bhaskarjyoti Das, Harshith Mohan Kumar, Divya Shekar, and Mohammed Zayd Jamadar
Springer Nature Singapore
Anind Kiran, Manah Shetty, Shreya Shukla, Varun Kerenalli, and Bhaskarjyoti Das
Springer Nature Singapore
Shravan Chandra and Bhaskarjyoti Das
IOP Publishing
AbstractWith society going online and disinformation getting accepted as a phenomena that we have to live with, there is a growing need to automatically detect offensive text on modern social media platforms. But the lack of enough balanced labeled data, constantly evolving socio-linguistic patterns and ever-changing definition of offensive text make it a challenging task. This is a common pattern witnessed in all disinformation detection tasks such as detection of propaganda, rumour, fake news, hate etc. The work described in this paper improves upon the existing body of techniques by bringing in an approach framework that can surpass the existing benchmarks. Firstly, it addresses the imbalanced and insufficient nature of available labeled dataset. Secondly, learning using relates tasks through multi-task learning has been proved to be an effective approach in this domain but it has the unrealistic requirement of labeled data for all related tasks. The framework presented here suitably uses transfer learning in lieu of multi-task learning to address this issue. Thirdly, it builds a model explicitly addressing the hierarchical nature in the taxonomy of disinformation being detected as that delivers a stronger error feedback to the learning tasks. Finally, the model is made more robust by adversarial training. The work presented in this paper uses offensive text detection as a case study and shows convincing results for the chosen approach. The framework adopted can be easily replicated in other similar learning tasks facing a similar set of challenges.
T N Lokesh Kumar and Bhaskarjyoti Das
IOP Publishing
AbstractAvailability of enough labeled data is a challenge for most inductive learners who try to generalize based on limited labeled dataset. A traditional semi-supervised approach for the same problem attempts to approach it by methods such as wrapping multiple inductive learners on derived pseudo-labels, unsupervised feature extraction or suitable modification of the objective function. In this work, a simple approach is adopted whereby an inductive learner is enhanced by suitably enabling it with a transductive view of the data. The experiments, though conducted on a small dataset, successfully provide few insights i.e. transductive view benefits an inductive learner, a transductive view that considers both attribute and relations is more effective than one that considers either attributes or relations and graph convolution based embedding algorithms effectively captures the information from transductive views compared to popular knowledge embedding approaches.
Paritosh Kapadia, Akrati Saxena, Bhaskarjyoti Das, Yulong Pei, and Mykola Pechenizkiy
Springer International Publishing
Bhaskarjyoti Das, Anvitha Poosarla, V. Mahima, and Chetana Kulkarni
Springer Nature Singapore
Meghana and Bhaskarjyoti Das
Springer Singapore
Aayushi Sanghi, Abhishek Sinha, Aditya Venkatesh, and Bhaskarjyoti Das
Springer Singapore
M. Guruprasad, Jai Agarwal, T. N. Lokesh Kumar, and Bhaskarjyoti Das
Springer Singapore
Akshara Subramaniasivam, Kaushik Ravichandran, Aishwarya Poomuttam Sreedas, and Bhaskarjyoti Das
Springer Singapore