Amit Kumar Jaiswal

amitkumarj441 [at] gmail [dot] com
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I am a final year PhD student as well as a Marie Curie Researcher in IRAC at the University of Bedfordshire, advised by Haiming Liu and co-advised by Ingo Frommholz. I research Quantum Information Retrieval. I study the quantum theory evolving behind information retrieval with focus on user-oriented IR based on Information Foraging Theory. My research spans a wide range of topics in this area, including existing Quantum-IR frameworks, mathematical formalism behind Quantum Theory; Quantum Probability, and their applications in the study of information retrieval (IR), interactive IR in general. My work is supported by EU Horizon 2020 research under the Marie Skłodowska-Curie grant No. 721321.

Before graduate school, I received a Bachelors in Computer Science & Engineering from UIET, CSJM University. I was also a part of Google Summer of Code program and the Linux Foundation as an intern with Kubernetes, CNCF and Openstack team at the Open Mainframe Project.

What's new?

Publications

Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Proceedings of Bridging the Gap between Information Science, Information Retrieval and Data Science (BIRDS) (SIGIR), 2020

Understanding an information forager's actions during interaction is very important to the study of interactive information retrieval. Although information spread in uncertain information space is substantially complex due to the high entanglement of users’ interacting with information objects (text, image, etc.) and vice versa. However, an information forager, in general, accompanies a piece of information (information diet) while searching (or foraging) alternative contents, typically subject to decisive uncertainty. ...

[ Preprint ]  [ Slides ]  [BIBTEX ]

Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Proceedings of 42th European Conference on Information Retrieval (ECIR), 2020

Query Auto-completion (QAC) is a prominently used feature in search engines, where user interaction with such explicit feature is facilitated by the possible automatic suggestion of queries based on a prefix typed by the user. Existing QAC models have pursued a little on user interaction and cannot capture a user’s information need (IN) context. ...

[ Paper ]  [Slides ]  [BIBTEX ]

Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Proceedings of the 11th annual meeting of the Forum for Information Retrieval Evaluation (FIRE), 2019

User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through implicit behavioural signals for image recommendation based on the Information Scent Model of Information Foraging Theory. In the first part, we hypothesise that the users' perception is improved with visual cues in the images as behavioural signals that provide users' information scent during information seeking. ...

[ Paper ]  [ Slides ]  [ BIBTEX ]

Effects of Foraging in Personalized Content-based Image Recommendation

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
The 2nd International Workshop on ExplainAble Recommendation and Search (EARS), SIGIR, 2019

A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with recommendations to personalize their navigation. However, few studies have addressed the question of what drives the users’ attention to specific content within the collection and what influences the selection of interesting items. To this end, we employ the lens of Information Foraging Theory (IFT) to image recommendation to demonstrate how the user could utilize visual bookmarks to locate interesting images. ...

[ Paper ]  [ Poster ]  [ Slides ]  [ BIBTEX ]

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases

Amit Kumar Jaiswal, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, Samuel Abramov
Workshop Towards Causal, Explainable and Universal Medical Visual Diagnosis, CVPR, 2019

Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologically organized. However, the task of finding metastatic tissues is gradual which is often challenging. In this work, we present our deep convolutional neural network based model validated on PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. ...

[ Paper ]  [ Poster ]  [ BIBTEX ]

Identifying pneumonia in chest X-rays: A deep learning approach

Amit Kumar Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna & J. Rodrigues
Journal of Measurement, 2019

The rich collection of annotated datasets piloted the robustness of deep learning techniques to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths include children under age five are caused by pneumonia globally. In this study, we describe our deep learning based approach for the identification and localization of pneumonia in Chest X-rays (CXRs) images. Researchers usually employ CXRs for the diagnostic imaging study. Several factors such as positioning of the patient and depth of inspiration can change the appearance of the chest X-ray, complicating interpretation further. ...

[ Paper ]  [ Code]  [ BIBTEX ]

Quantum-like Generalization of Complex Word Embedding: A Lightweight Approach for Textual Classification

Amit Kumar Jaiswal, Guilherme Holdack, Ingo Frommholz, Haiming Liu
FGIR, LWDA, 2018

In this paper, we present an extension, and an evaluation, to existing Quantum like approaches of word embedding for IR tasks that (1) improves complex features detection of word use (e.g., syntax and semantics), (2) enhances how this method extends these aforementioned uses across linguistic contexts (i.e., to model lexical ambiguity) - specifically Question Classification -, and (3) reduces computational resources needed for training and operating Quantum based neural networks, when confronted with existing models. ...

[ Paper ]  [ BIBTEX ]

Parsec: A State Channel for the Internet of Value

Amit Kumar Jaiswal
Ongoing Work, 2017

We propose Parsec, a web-scale State channel for the Internet of Value to exterminate the consensus bottleneck in Blockchain by leveraging a network of state channels which enable to robustly transfer value off-chain. It acts as an infrastructure layer developed on top of Ethereum Blockchain, as a network protocol which allows coherent routing and interlocking channel transfers for trade-off between parties. A web-scale solution for state channels is implemented to enable a layer of value transfer to the internet. Existing network protocol on State Channels include Raiden for Ethereum and Lightning Network for Bitcoin. ...

[ Preprint ]  [ Code ]  [ BIBTEX ]

Talks

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