Bhaskarjyoti Das

@pes.edu

Adjunct Professor , Department of Computer Science and Engineering in AI and ML
PES University



                 

https://researchid.co/bjdpes2023

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science Applications

57

Scopus Publications

147

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Meta-learning in Audio and Speech Processing: An End to End Comprehensive Review
    Athul Raimon, Shubha Masti, Shyam K. Sateesh, Siyani Vengatagiri, and Bhaskarjyoti Das
    Springer Nature Singapore

  • Meta-Learning-Based Multimodal Fake News Detection
    M. S. Varun, Ishaan Bhattacharjee, Pratheek Karike Venkatesha, A. Sathvik, S. Pramath, and Bhaskarjyoti Das
    Springer Nature Singapore

  • The tale of two sides in the 2019 anti-CAA protest—An analytical framework
    Bhaskarjyoti Das, Krithika Ragothaman, Raghav T. Kesari, and Sudarshan T.S.B.
    Elsevier BV

  • Interpretation of White Box Adversarial Attacks on Machine Learning Model Using Grad-CAM
    Ug Dheeraj Sai, Vinay Sai Yogeesh, N Vindya, Akanksha P Mulgund, and Bhaskarjyoti Das
    IEEE
    ML models are now finding extensive applications in sensitive fields such as healthcare. These models contribute significantly to diagnosis and decision-making. However, they suffer from adversarial attacks wherein small, often imperceptible, changes in input data produce undesirable outputs. Such vulnerabilities can further threaten patient safety and privacy. We conduct this study to interpret adversarial attack impact in CNN model using Grad-CAM. Grad-CAM is a tool that visualizes the model’s decision making through highlighting parts of an input image that influence its predictions. We have used SqueezeNet model, a lightweight convolutional network for the classification of ophthalmic images which includes 4 classes - normal, cataract, diabetic retinopathy, and glaucoma. We then subjected our model to four standard attacks: the Fast Gradient Sign Method, Projected Gradient Descent, Basic Iterative method and Carlini Wagner attack. Our experiments show that each attack heavily distorts the attentions induced in the model by Grad-CAM, and it, in turn, produces incorrect classifications. Therefore, such findings emphasize the need for developing stronger ML models as well as the utility of interpretability tools in explaining and countering adversarial threats. Future work will comprise integration of insights provided by Grad-CAM together with better detection systems to enhance the resilience of the model.

  • Statistically Inspired Discrepancy Detection for Anomalous Spatio-Temporal Graphs (Extended Abstract)
    Atharv Tiwari, Shreyash Chatterjee, Siddharth Padmakumar, Sushanth Nair, and Bhaskarjyoti Das
    IEEE
    Anomaly detection in dynamic graphs is a critical topic with applications in many fields, such as fraud detection and network security. This paper tackles the difficulties in locating abnormalities in time-varying graphs by presenting a novel divide-and-conquer method. We combine Graph Convolutional Networks (GCN) and Recurrent Neural Networks (RNN) to predict future node values on temporal graphs, followed by a macro and micro-level analysis. At the macro level, we present a novel algorithm to extract correlation-based subgraphs. The values obtained for each subgraph allow us to concentrate on subgraphs that show significant anomalies, effectively minimizing the challenges in node-level anomaly detection. This is followed by a micro-level analysis of the node contributions and temporal properties within the identified anomalous subgraphs. By combining macro and micro-level analysis with a machine learning-based approach, our method provides an efficient approach for zoning in on anomalies, significantly speeding up computation.

  • Statistically inspired discrepancy detection for anomalous spatio-temporal graphs
    Atharv Tiwari, Shreyash Chatterjee, Siddharth Padmakumar, Sushanth Nair, and Bhaskarjyoti Das
    Springer Science and Business Media LLC

  • Understanding the Psychological Needs at Play in Disinformation
    Kaveesh Khattar and Bhaskarjyoti Das
    Springer Nature Singapore


  • Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks
    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.


  • Detection of Conversational Health in a Multimodal Conversation Graph by Measuring Emotional Concordance
    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.

  • Mutual Consumer Behavioral Community Detection in Facebook's Large Page-Page Dataset Using Graph/SubGraph Analysis
    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.

  • An Ensemble Approach Towards Correlating Articles and their Corresponding Images


  • A Graph-Based Relook Beyond Metadata for Music Recommendation
    Vishal Bharadwaj, Aravind S. Mysore, Ninad Sangli, Shraddha Bharadwaj, and Bhaskarjyoti Das
    Springer Nature Singapore

  • An Interpretability Assisted Empirical Study of Affective Traits in Visual Content of Disinformation
    Bhaskarjyoti Das, Shrikar Madhu, Yousha Mahamuni, and Kruthika Suresh
    Springer Nature Singapore

  • DynaSim: A Ball-by-Ball Simulation of the Dynamics of an IPL T20 Game
    Aravind Subramanya Mysore, Vishal Bharadwaj, Rithik R. Mali, and Bhaskarjyoti Das
    Springer Nature Singapore

  • Deep QA: An Open-Domain Dataset of Deep Questions and Comprehensive Answers
    Hariharasudan Savithri Anbarasu, Harshavardhan Veeranna Navalli, Harshita Vidapanakal, K. Manish Gowd, and Bhaskarjyoti Das
    Springer Nature Switzerland

  • An Interpretable Hybrid Recommender Based on Graph Convolution to Address Serendipity
    Ananya Uppal, P. Maitreyi, P. Shreya, Trisha Jain, and Bhaskaryoti Das
    Springer Nature Switzerland

  • A Dataset and Multi-task Multi-view Approach for Question-Answering with the Dual Perspectives of Text and Knowledge
    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.

  • Semi-supervised Learning with In-domain Pre-training and Deep Co-training
    Bhaskarjyoti Das, Harshith Mohan Kumar, Divya Shekar, and Mohammed Zayd Jamadar
    Springer Nature Singapore

  • Getting Around the Semantics Challenge in Hateful Memes
    Anind Kiran, Manah Shetty, Shreya Shukla, Varun Kerenalli, and Bhaskarjyoti Das
    Springer Nature Singapore

  • An approach framework of transfer learning, adversarial training and hierarchical multi-task learning - A case study of disinformation detection with offensive text
    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.

  • An evaluation of approaches for enhancing inductive learning with a transductive view
    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.

  • Co-Attention Based Multi-contextual Fake News Detection
    Paritosh Kapadia, Akrati Saxena, Bhaskarjyoti Das, Yulong Pei, and Mykola Pechenizkiy
    Springer International Publishing

  • Learning Conversational Stance from Multiple Views of Social Graph Using Multitask and Other Approaches
    Bhaskarjyoti Das, Anvitha Poosarla, V. Mahima, and Chetana Kulkarni
    Springer Nature Singapore

RECENT SCHOLAR PUBLICATIONS

  • Meta-learning in Audio and Speech Processing: An End to End Comprehensive Review
    A Raimon, S Masti, SK Sateesh, S Vengatagiri, B Das
    International Conference on Multi-disciplinary Trends in Artificial 2024

  • The tale of two sides in the 2019 anti-CAA protest—An analytical framework
    B Das, K Ragothaman, RT Kesari, S TSB
    International Journal of Information Management Data Insights 4 (2), 100300 2024

  • Statistically Inspired Discrepancy Detection for Anomalous Spatio-Temporal Graphs (Extended Abstract)
    B Tiwari, Atharv and Chatterjee, Shreyash and Padmakumar, Siddharth and Nair ...
    2024 IEEE 11th International Conference on Data Science and Advanced 2024

  • Statistically Inspired Discrepancy Detection for Anomalous Spatio-Temporal Graphs
    A Tiwari, S Chatterjee, S Padmakumar, S Nair, B Das
    International Journal of Data Science and Analytics 2024

  • A Dataset of Multi-modal satire - humorous and non-humorous
    B Das, R Acharya, U Roshan Kumar, M Varun Kumar, D Viresh
    10.5281/zenodo.10613173 2024

  • Emulating Emergency Vehicle Navigation in Mixed Traffic Environment Using Graph Prediction and Simulation
    S S, AN Rao, S B U, S M P, B Das
    Proceeding of International Conference on Advanced Data Driven Intelligence 2024

  • Video Manipulation Detection using Sequence Learning and Convolution Networks: A Comparative Study
    R Kurian, R Shahi, R Singh, V Goel, B Das
    Proceeding of International Conference on Advanced Data Driven Intelligence 2024

  • ReCap Pro: Caption Correction using Meta Learning
    S Birthi, S Mahesh, S Mathuria, SJ Chilkund, B Das
    Proceeding of International Conference on Advanced Data Driven Intelligence 2024

  • A Federated Learning approach to Question Answering on Knowledge Graphs
    A Gunti, A Patil, A Narayan, A Gulati, B Das
    Proceeding of International Conference on Advanced Data Driven Intelligence 2024

  • Detection of Propaganda Techniques in Social Media Texts by Lifelong Learning
    T N, S Kishor, S Karande, V Neha, B Das
    Proceeding of International Conference on Advanced Data Driven Intelligence 2024

  • Meta Learning Assisted Graph Representation Learning for Downstream Learning Tasks
    NR Bhat, PS Kulkarni, A Menon, SS Meda, B Das
    CoCoNet 2023 2024

  • Meta Learning Based Multimodal Fake News Detection
    V MS, I Bhattacharjee, P Karike Venkatesha, S A, P S, B Das
    CoCoNet 2023 2024

  • Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks
    S Acharya, B Das, TSB Sudarshan
    IEEE Access 12, 1250-1265 2023

  • Dataset of conversation graphs from 2019 Anti CAA protest in India
    B Das, R Krithika, RT Kesari
    10.5281/zenodo.10212106 2023

  • A Graph-Based Relook Beyond Metadata for Music Recommendation
    V Bharadwaj, AS Mysore, N Sangli, S Bharadwaj, B Das
    Innovations in Computational Intelligence and Computer Vision: Proceedings 2023

  • An Interpretability Assisted Empirical Study of Affective Traits in Visual Content of Disinformation
    B Das, S Madhu, Y Mahamuni, K Suresh
    International Conference on Innovations in Computational Intelligence and 2023

  • DynaSim: A Ball-by-Ball Simulation of the Dynamics of an IPL T20 Game
    AS Mysore, V Bharadwaj, RR Mali, B Das
    Congress on Smart Computing Technologies, 377-394 2023

  • Deep QA: An Open-Domain Dataset of Deep Questions and Comprehensive Answers
    HS Anbarasu, HV Navalli, H Vidapanakal, KM Gowd, B Das
    International Conference on Computer and Communication Engineering, 189-202 2023

  • Multi-contextual learning in disinformation research: A review of challenges, approaches, and opportunities
    B Das, S TSB
    Online Social Networks and Media 34, 100247 2023

  • Multi-label Tweet Dataset for Textual Propaganda Detection related to anti-CAA protest in India (2019-2021)
    B Das, N Nikhila Priya, SD Kakumani, V Goyal
    10.5281/zenodo.7797035 2023

MOST CITED SCHOLAR PUBLICATIONS

  • Proof of stack consensus for blockchain networks
    A Barhanpure, P Belandor, B Das
    International Symposium on Security in Computing and Communication, 104-116 2018
    Citations: 25

  • A computational analysis of Mahabharata
    D Das, B Das, K Mahesh
    Proceedings of the 13th International Conference on Natural Language 2016
    Citations: 14

  • Multi-contextual learning in disinformation research: A review of challenges, approaches, and opportunities
    B Das, S TSB
    Online Social Networks and Media 34, 100247 2023
    Citations: 10

  • Character level neural architectures for boosting named entity recognition in code mixed tweets
    A Narayanan, A Rao, A Prasad, B Das
    2020 International Conference on Emerging Trends in Information Technology 2020
    Citations: 10

  • Text segregation on asynchronous group chat
    A Sinha, M Manohar T, S Subramanian, B Das
    Procedia Computer Science 2019
    Citations: 9

  • Extending the performance of extractive text summarization by ensemble techniques
    A Bharadwaj, A Srinivasan, A Kasi, B Das
    11th INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING 2019
    Citations: 7

  • Deep detection of anomalies in static attributed graph
    PG Kulkarni, SY Praneet, RB Raghav, B Das
    Machine Learning, Image Processing, Network Security and Data Sciences 2020
    Citations: 6

  • Analysing conflicts in online football communities of reddit
    S Aswath, D Godavarthi, B Das
    2020 international conference on emerging trends in information technology 2020
    Citations: 5

  • A study of artificial social intelligence in conversational agents
    V Satyanarayana, S Shankar, V Sruthi, B Das
    2018 3rd International Conference on Inventive Computation Technologies 2018
    Citations: 5

  • Emotion enhanced domain adaptation for propaganda detection in Indian social media
    M Rajmohan, R Kamath, AP Reddy, B Das
    Innovations in Computational Intelligence and Computer Vision: Proceedings 2022
    Citations: 4

  • An approach framework of transfer learning, adversarial training and hierarchical multi-task learning-a case study of disinformation detection with offensive text
    S Chandra, B Das
    Journal of Physics: Conference Series 2161 (1), 012049 2022
    Citations: 4

  • Graph Convolution-Based Joint Learning of Rumor with Content, User Credibility, Propagation Context, and Cognitive as Well as Emotion Signals
    P Nagaraj, B Das
    Sentimental Analysis and Deep Learning, 113-128 2022
    Citations: 4

  • Graph embedding based hybrid social recommendation system
    V Sathish, T Mehrotra, S Dhinwa, B Das
    arXiv preprint arXiv:1908.09454 2019
    Citations: 4

  • Predicting Protein-Protein Interaction in Multi-layer Blood Cell PPI Networks
    P Kapadia, S Khare, P Priyadarshini, B Das
    International Conference on Advanced Informatics for Computing Research, 240-251 2019
    Citations: 4

  • Co-Attention Based Multi-contextual Fake News Detection
    P Kapadia, A Saxena, B Das, Y Pei, M Pechenizkiy
    Complex Networks XIII: Proceedings of the 13th Conference on Complex 2023
    Citations: 3

  • Multi Task Learning For Financial Forecasting
    D Prabhu, A Chhabra, S Goyal, B Das
    2nd INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES IN DATA MINING AND 2020
    Citations: 3

  • A Framework to Capture the Shift in Dynamics of a Multi-phase Protest - Case Study of Hong Kong Protests
    A Sanghi, A Sinha, A Venkatesh, B Das
    2nd INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES IN DATA MINING AND 2020
    Citations: 3

  • Twitter as a mirror-perspectives of common men and key personalities
    A Kumar, D Mishra, B Das
    2020 International Conference on Emerging Trends in Information Technology 2020
    Citations: 3

  • Extraction of key topics from online text reviews
    B Das, R PV
    International Journal of Emerging Trends & Technology in Computer Science 5 2016
    Citations: 3

  • Getting around the semantics challenge in hateful memes
    A Kiran, M Shetty, S Shukla, V Kerenalli, B Das
    Computational Intelligence and Data Analytics: Proceedings of ICCIDA 2022 2022
    Citations: 2