arXiv subjects

Kislay Raj

Publications and source records attributed to Kislay Raj.

4 recordsLinked to original sources

arXiv2026

Bigger Isn't Always Better: A Comparative Evaluation of LLMs for Automated Code Review

We present a systematic evaluation of five large language models on automated code review, comparing Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4 mini, Minimax M2.7, and GLM-5 Turbo across 150 code review samples - 100 synthetic mutation-injected bugs and 50 real bug-fix pull requests mined from eight major open-source repositories. Our principal finding is that Claude Haiku 4.5, a smaller and cheaper model, consistently outperforms the larger Claude Sonnet 4.6, achieving higher F1 (0.365 vs. 0.343), 18% higher recall, and superior qualitative review scores across all four evaluation dimensions, at 3.2x lower cost per review. This result holds across three independent experimental conditions (n=25, n=100, n=150) and is independently confirmed on the Martian Code Review Benchmark, a third-party evaluation with different repos, golden comments, and judge. We further report three secondary findings: (1) synthetic-only evaluation dramatically overestimates model capability - on real PRs alone, the best model achieves F1 = 0.066, compared to F1 = 0.847 on synthetic samples, a 92% degradation; (2) diff size is the dominant predictor of review quality, with F1 dropping from 0.657 on diffs under 10 lines to 0.043 on diffs over 150 lines; and (3) all models exhibit near-zero recall on performance-related bugs. We release our evaluation framework and dataset for reproducibility.

cs.SE↗

arXiv2024

OxML Challenge 2023: Carcinoma classification using data augmentation

Carcinoma is the prevailing type of cancer and can manifest in various body parts. It is widespread and can potentially develop in numerous locations within the body. In the medical domain, data for carcinoma cancer is often limited or unavailable due to privacy concerns. Moreover, when available, it is highly imbalanced, with a scarcity of positive class samples and an abundance of negative ones. The OXML 2023 challenge provides a small and imbalanced dataset, presenting significant challenges for carcinoma classification. To tackle these issues, participants in the challenge have employed various approaches, relying on pre-trained models, preprocessing techniques, and few-shot learning. Our work proposes a novel technique that combines padding augmentation and ensembling to address the carcinoma classification challenge. In our proposed method, we utilize ensembles of five neural networks and implement padding as a data augmentation technique, taking into account varying image sizes to enhance the classifier's performance. Using our approach, we made place into top three and declared as winner.

cs.CV↗

arXiv2023

Understanding EEG signals for subject-wise Definition of Armoni Activities

In a growing world of technology, psychological disorders became a challenge to be solved. The methods used for cognitive stimulation are very conventional and based on one-way communication, which only relies on the material or method used for training of an individual. It doesn't use any kind of feedback from the individual to analyze the progress of the training process. We have proposed a closed-loop methodology to improve the cognitive state of a person with ID (Intellectual disability). We have used a platform named 'Armoni', for providing training to the intellectually disabled individuals. The learning is performed in a closed-loop by using feedback in the form of change in affective state. For feedback to the Armoni, an EEG (Electroencephalograph) headband is used. All the changes in EEG are observed and classified against the change in the mean and standard deviation value of all frequency bands of signal. This comparison is being helpful in defining every activity with respect to change in brain signals. In this paper, we have discussed the process of treatment of EEG signal and its definition against the different activities of Armoni. We have tested it on 6 different systems with different age groups and cognitive levels.

eess.SP↗

arXiv2022

SQL and NoSQL Databases Software architectures performance analysis and assessments -- A Systematic Literature review

Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, where competent software architecture plays a vital role. The SQL Databases are designed for structuring data and supporting vertical scalability. In contrast, horizontal scalability is backed by NoSQL Databases and can process sizeable unstructured Data efficiently. One can choose the right paradigm according to the organisation's needs; however, making the correct choice can often be challenging. The SQL and NoSQL Databases follow different architectures. Also, the mixed model is followed by each category of NoSQL Databases. Hence, data movement becomes difficult for cloud consumers across multiple cloud service providers (CSPs). In addition, each cloud platform IaaS, PaaS, SaaS, and DBaaS also monitors various paradigms. Objective: This systematic literature review (SLR) aims to study the related articles associated with SQL and NoSQL Database software architectures and tackle data portability and Interoperability among various cloud platforms. State of the art presented many performance comparison studies of SQL and NoSQL Databases by observing scaling, performance, availability, consistency and sharding characteristics. According to the research studies, NoSQL Database designed structures can be the right choice for big data analytics, while SQL Databases are suitable for OLTP Databases. The researcher proposes numerous approaches associated with data movement in the cloud. Platform-based APIs are developed, which makes users' data movement difficult. Therefore, data portability and Interoperability issues are noticed during data movement across multiple CSPs. To minimize developer efforts and Interoperability, Unified APIs are demanded to make data movement relatively more accessible among various cloud platforms.

cs.DB↗