About

University roles and responsibilities

  • Postdoctoral Research Fellow

    Previous roles

    01 April 2022 - 31 March 2023
    Postdoctoral Research Fellow
    University College London
    2021 - 2021
    Research Software Engineer, Department of Statistics, School of Mathematics
    University of Leeds

    Research

    Research interests

    Publications

    Eduard Goean, Xavier Font, Yu Xiong, Susanne Becken, Jonathan Chenoweth, Lorenzo Fioramonti, James Higham, Amit Jaiswal, Jhuma Sadhukhan, Sun Ya-Yen, Horst Treiblmaier, Senmao Xia, Xun Zhou (2024)Using the Blockchain to Reduce Carbon Emissions in the Visitor Economy, In: Sustainability16(10)4000 MDPI AG

    The visitor economy is responsible for a substantial percentage of the global carbon footprint. The mechanisms used to decarbonize it are insufficient, and the industry is relying on carbon trading with substandard credits that allow businesses to outsource the responsibility to decarbonize. We aim to transform carbon markets, help finance climate investments, and support decarbonization strategies. We identify and define the problem, outline the components and their interactions, and develop a conceptual model to transform carbon markets. The new, blockchain-based Carbon Tokenomics Model rolls out a decentralized database to store, trade, and manage carbon credits, with the goal of enabling sustainable climate finance investment. We outline the criteria needed for an industry-wide carbon calculator. We explain the process needed to increase rigor in climate investments in the visitor economy and introduce a delegated Proof of Commitment consensus mechanism. Our inclusive and transparent model illustrates how to reduce transaction costs and how to build consumer and industry trust, generating much-needed investments for decarbonization.

    Amit Kumar Jaiswal, Yu Xiong (2023)A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings, In: Seventeenth ACM Conference on Recommender Systems (RecSys ’23), September 18–22, 2023, Singapore, Singaporepp. 1190-1195 Association for Computing Machinery (ACM)

    Item representation holds significant importance in recommendation systems, which encompasses domains such as news, retail, and videos. Retrieval and ranking models utilise item representation to capture the user-item relationship based on user behaviours. While existing representation learning methods primarily focus on optimising item-based mechanisms, such as attention and sequential modelling. However, these methods lack a modelling mechanism to directly reflect user interests within the learned item representations. Consequently, these methods may be less effective in capturing user interests indirectly. To address this challenge, we propose a novel Interest-aware Capsule network (IaCN) recommendation model, a model-agnostic framework that directly learns interest-oriented item representations. IaCN serves as an auxiliary task, enabling the joint learning of both item-based and interest-based representations. This framework adopts existing recommendation models without requiring substantial redesign. We evaluate the proposed approach on benchmark datasets, exploring various scenarios involving different deep neural networks, behaviour sequence lengths, and joint learning ratios of interest-oriented item representations. Experimental results demonstrate significant performance enhancements across diverse recommendation models, validating the effectiveness of our approach.

    Amit Kumar Jaiswal, Haiming Liu (2023)Lightweight Adaptation of Neural Language Models via Subspace Embedding, In: CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM ’23), October 21–25, 2023, Birmingham, United Kingdompp. 3968-3972 Association for Computing Machinery (ACM)

    Traditional neural word embeddings are usually dependent on a richer diversity of vocabulary. However, the language models recline to cover major vocabularies via the word embedding parameters, in particular, for multilingual language models that generally cover a significant part of their overall learning parameters. In this work, we present a new compact embedding structure to reduce the memory footprint of the pre-trained language models with a sacrifice of up to 4% absolute accuracy. The embeddings vectors reconstruction follows a set of subspace embeddings and an assignment procedure via the contextual relationship among tokens from pre-trained language models. The subspace embedding structure1 calibrates to masked language models, to evaluate our compact embedding structure on similarity and textual entailment tasks, sentence and paraphrase tasks. Our experimental evaluation shows that the subspace embeddings achieve compression rates beyond 99.8% in comparison with the original embeddings for the language models on XNLI and GLUE benchmark suites.

    Gautam Kishore Shahi, Amit Kumar Jaiswal, Thomas Mandl (2024)FakeClaim: A Multiple Platform-Driven Dataset for Identification of Fake News on 2023 Israel-Hamas War, In: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part V Springer

    We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification. The FakeClaim data is collected from 60 fact-checking organizations in 30 languages and enriched with metadata from the fact-checking organizations curated by trained journalists specialized in fact-checking. Further, we classify fake videos within the subset of YouTube videos using textual information and user comments. We used a pre-trained model to classify each video with different feature combinations. Our best-performing fine-tuned language model, Universal Sentence Encoder (USE), achieves a Macro F1 of 87%, which shows that the trained model can be helpful for debunking fake videos using the comments from the user discussion.

    Item representation holds significant importance in recommendation systems, which encompasses domains such as news, retail, and videos. Retrieval and ranking models utilise item representation to capture the user-item relationship based on user behaviours. While existing representation learning methods primarily focus on optimising item-based mechanisms, such as attention and sequential modelling. However, these methods lack a modelling mechanism to directly reflect user interests within the learned item representations. Consequently, these methods may be less effective in capturing user interests indirectly. To address this challenge, we propose a novel Interest-aware Capsule network (IaCN) recommendation model, a model-agnostic framework that directly learns interest-oriented item representations. IaCN serves as an auxiliary task, enabling the joint learning of both item-based and interest-based representations. This framework adopts existing recommendation models without requiring substantial redesign. We evaluate the proposed approach on benchmark datasets, exploring various scenarios involving different deep neural networks, behaviour sequence lengths, and joint learning ratios of interest-oriented item representations. Experimental results demonstrate significant performance enhancements across diverse recommendation models, validating the effectiveness of our approach.