Rashmi Gupta

Aakash Kumar Agarwal, Saprativa Bhattacharjee, Mauli Rastogi et al.

Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination into both annotation and modeling. Through statistical analyses and machine learning experiments, we demonstrate that cognitive markers significantly differentiate relapse and non-relapse groups, and that models enriched with these features achieve competitive performance, with transformer-based temporal models attaining an F1 of 0.86. Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection, paving the way for scalable, low-cost interventions in mental healthcare.

Rashmi Gupta, Aditya K Gupta, Aarav Jain et al.

Like any other discipline, Large Language Models (LLMs) have significantly impacted software engineering by helping developers generate the required artifacts across various phases of software development. This paper presents a case study comparing the performance of popular LLMs GPT, Claude, Gemini, and DeepSeek in generating functional specifications that include use cases, business rules, and collaborative workflows for a web application, the Mess Management System. The study evaluated the quality of LLM generated use cases, business rules, and collaborative workflows in terms of their syntactic and semantic correctness, consistency, non ambiguity, and completeness compared to the reference specifications against the zero-shot prompted problem statement. Our results suggested that all four LLMs can specify syntactically and semantically correct, mostly non-ambiguous artifacts. Still, they may be inconsistent at times and may differ significantly in the completeness of the generated specification. Claude and Gemini generated all the reference use cases, with Claude achieving the most complete but somewhat redundant use case specifications. Similar results were obtained for specifying workflows. However, all four LLMs struggled to generate relevant Business Rules, with DeepSeek generating the most reference rules but with less completeness. Overall, Claude generated more complete specification artifacts, while Gemini was more precise in the specifications it generated.

Rashmi Gupta, Nisheeth Joshi, Iti Mathur

In this paper we present an approach for estimating the quality of machine translation system. There are various methods for estimating the quality of output sentences, but in this paper we focus on Naïve Bayes classifier to build model using features which are extracted from the input sentences. These features are used for finding the likelihood of each of the sentences of the training data which are then further used for determining the scores of the test data. On the basis of these scores we determine the class labels of the test data.

Rashmi Gupta, Nisheeth Joshi, Iti Mathur

This paper considers the problem for estimating the quality of machine translation outputs which are independent of human intervention and are generally addressed using machine learning techniques.There are various measures through which a machine learns translations quality. Automatic Evaluation metrics produce good co-relation at corpus level but cannot produce the same results at the same segment or sentence level. In this paper 16 features are extracted from the input sentences and their translations and a quality score is obtained based on Bayesian inference produced from training data.

Rashmi Gupta, Shalini Raghav

The Rational Unified Process a software engineering process is gaining popularity nowadays. RUP delivers best software practices for component software Development life cycle It supports component based software development. Risk is involved in every component development phase .neglecting those risks sometimes hampers the software growth and leads to negative outcome. In Order to provide appropriate security and protection levels, identifying various risks is very vital. Therefore Risk identification plays a very crucial role in the component based software development This report addresses incorporation of component based software development cycle into RUP phases, assess several category of risk encountered in the component based software. It also entails a survey method to identify the risk factor and evaluating the overall severity of the component software development in terms of the risk. Formula for determining risk prevention cost and finding the risk probability is also been included. The overall goal of the paper is to provide a theoretical foundation that facilitates a good understanding of risk in relation to componentbased system development