Browsing by Author "Mayya, S."

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    DBNLP: detecting bias in natural language processing system for India-centric languages
    (Springer Science and Business Media B.V., 2025) Keerthan Kumar, K.K.; Mendke, S.; Parihar, R.; Mayya, S.; Venkatesh, S.; Koolagudi, S.G.
    Natural language processing (NLP) is gaining widespread interest and seeing advancements rapidly due to its attractive and exhilarating applications. NLP models are being developed in search engines for real-world scenarios such as language translation, sentiment analysis, chat-bots such as ChatGPT, and auto-completion. These models are trained on a vast corpus of online data, exposing them to harmful biases and stereotypes towards various communities. The models learn these biases, making harmful and undesirable predictions about particular genders, religions, races, and professions. Biases in NLP systems can perpetuate societal biases and discrimination, leading to unfair and unequal treatment of individuals or groups. It is crucial to identify these biases, which will help mitigate them. Most of the literary works in this area have been primarily Western-centric, focusing on the English language, making it tough to use them for Indian models and languages. In this work, we propose a model called Detecting Bias in Natural Language Processing System for India-Centric Languages (DBNLP), which aims to identify the biases relevant to the Indian context present in the text-based language models, particularly for the English and Hindi languages. The DBNLP presents three techniques for bias identification based on (1) a Context Association Test (CAT), (2) a template-based perturbation technique for various co-domain associations, and (3) a co-occurrence count-based corpus analysis technique. Further, this work showcases how India-centric models such as IndicBERT, MuRIL, and datasets such as IndicCorp are biased toward various demographic categories. Detecting bias in natural language processing systems for India-centric languages is essential to creating fair, diverse, and inclusive models that benefit society. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
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    Investigation of Field Dependent Variations of Torsional Stiffness of Magnetorheological Elastomer
    (American Institute of Physics, 2024) Kaup, P.S.; Kumar, S.; Kamath, N.; Mayya, S.; Gangadharan, K.V.
    Isolation of torsional vibrations in shafts is one of the most important aspects of a sound design system. Though existing systems such as the centrifugal pendulum absorber and the flywheels reduce the effects to a certain extent, the system fails to comply when the natural frequency of the torsional system changes. To counteract such instances, smart materials are used to tune their parameters based on the variations in the system variables. Magnetorheological Elastomers offer a viable solution to the dynamic vibrations as they can adhere to variations in system properties. To properly implement the MRE, it is mandatory to characterize the mechanical properties under dynamic loading conditions under varying magnetic fields. The present paper focuses on characterizing the torsional stiffness of the MRE under varying magnetic fields. The characterization methodology is discussed with the building of the measurement system, followed by the results and discussions of varying hysteresis loops for different magnetic fields. Variations in the properties are discussed, highlighting the role of the dipole mechanism. © 2024 American Institute of Physics Inc.. All rights reserved.