Ipsita Mohanty

Ipsita Mohanty

Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. This work proposes a novel framework for automated defect triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels fused text embeddings to improve contextual representations from human-generated product defects. For our multi-label text classification defect triage task, we also introduce a Walmart proprietary dataset of product defects using weak supervision and adversarial learning, in a few-shot setting.

Ipsita Mohanty, Ankit Goyal, Alex Dotterweich

Contrastive learning techniques have been widely used in the field of computer vision as a means of augmenting datasets. In this paper, we extend the use of these contrastive learning embeddings to sentiment analysis tasks and demonstrate that fine-tuning on these embeddings provides an improvement over fine-tuning on BERT-based embeddings to achieve higher benchmarks on the task of sentiment analysis when evaluated on the DynaSent dataset. We also explore how our fine-tuned models perform on cross-domain benchmark datasets. Additionally, we explore upsampling techniques to achieve a more balanced class distribution to make further improvements on our benchmark tasks.

Ipsita Mohanty, R. Leela Velusamy

Advanced internet technologies providing services like e-mail, social networking, online banking, online shopping etc., have made day-to-day activities simple and convenient. Increasing dependency on the internet, convenience, and decreasing cost of electronic devices have resulted in frequent use of online services. However, increased indulgence over the internet has also accelerated the pace of digital crimes. The increase in number and complexity of digital crimes has caught the attention of forensic investigators. The Digital Investigators are faced with the challenge of gathering accurate digital evidence from as many sources as possible. In this paper, an attempt was made to recover digital evidence from a system's RAM in the form of information about the most recent browsing session of the user. Four different applications were chosen and the experiment was conducted across two browsers. It was found that crucial information about the target user such as, user name, passwords, etc., was recoverable.