Abstract

This thesis investigates how entities are shaped by the discourse in which they appear. Rather than being isolated mentions, entities take on meaning through their interactions with the surrounding context, both in terms of reference and interpretation: the identity of an entity can shift depending on how it is invoked, and the narrative framing around it can significantly influence how it is perceived by a reader. At its core, the research addresses these two complementary dimensions of entity analysis: the challenge of tracking entity references across text (coreference resolution) and the interpretive challenge of understanding how entities are characterized through their framing within the text (entity role classification). While these tasks represent distinct technical problems, they are unified by their contribution to richer discourse comprehension and their shared goal of moving NLP systems beyond surface-level language processing toward deeper semantic understanding. The first part of this research focuses on coreference resolution for South Asian languages, addressing a significant gap in current NLP research where these morphologically rich and syntactically diverse languages remain underrepresented. To overcome the lack of suitable evaluation resources, we develop mGAP, a novel dataset containing coreference annotations across 27 South Asian languages, created through a dedicated translation and alignment pipeline from English reference data. This dataset provided the foundation for investigating cross-lingual transfer learning strategies designed to address the low-resource nature of many of these languages. Through systematic experimentation, we examine which languages serve as effective source languages for training when target language data is scarce, offering insights into making use of linguistic similarities for enhanced cross-lingual adaptation. The second part shifts focus to entity framing in news media, where the research question evolves from identifying what an entity refers to, to understanding what narrative role that entity plays within the text. The framing of the text is pivotal in how a reader would perceive the entity being described. For this task, we developed a two-stage pipeline for classifying entities into roles such as protagonist, antagonist, and innocent, as well as more fine-grained narrative categories. The pipeline first employs entity-centric summarization to extract relevant contextual information surrounding target entities, followed by classification using a DeBERTa v3-based model enhanced with contrastive learning techniques. This approach addresses the significant challenge of determining how entities are portrayed based on surrounding textual cues, contributing further to computational methods for analyzing media bias and narrative construction.

Abstract

India is experiencing an innovation upswing in many fields such as: pharmaceuticals, agriculture, space technology and especially in the area of IT/computer science. The Indian IT industry has a lot to offer: lower human resource costs, large talent pool, favourable government policies, access to capital, and advanced infrastructure that makes India the cost-effective Silicon Valley of the world. It also hosts a significant number of world-class research institutions producing handsome volumes of deep-tech computer science research. However, it has been observed that much of this research is unable to productise and become usable for markets in the real world This has resulted in a high volume of research with potential to disrupt markets but fail to carry through the ‘lab to land’ journey for a research output. This thesis is a study to understand the translation process of a research as a marketable product and to identify factors that impede or enable this translation. The productisation window for any relevant computer science research work is largely time sensitive. [1] Recent explosion in AI adoption fueled with a digital infrastructure has made many deep-tech Computer Science research areas to get production ready very fast. It has an associated shelf life during which it should be explored and leveraged for any sort of translational or productisation activity. Therefore, as a community engaging closely with research problems bridging disconnections between research labs and industry/market requires critical and timely interventions. While there are many research translation models available as a concept or practice, many are ineffective in explaining a seamless / smooth transition of any research work into an industry-consumable technology or product. Based on tacit knowledge & existing models of translation it is apparent to observe that due to the nature of the research ecosystem, its processes and the associated stakeholders within it, identifying potential translation flow is far from trivial. In order to understand the entire journey of a potential research translation, this thesis proposes a productisation flow that considers all potential channels through which a research work in computer science can transition into a market relevant product or an industry consumable technology. This thesis will cover the productisation flow in detail in the later chapters. In this study, we focus on understanding this gap in productisation from the perspective of research scholars who actually work on these projects. After an extensive literature study, the thesis focuses on highlighting the gap in considering ‘researchers’ as an active stakeholder in this entire productisation process. As a result, this thesis proposes a productisation flow of deep-tech research from lab to market translations that highlights the participation of researchers across various translational channels and their stages. This qualifies the need to understand reasons why research flow from lab to land is enabled or inhibited wrt researchers.

Abstract

This study critically analyses portrayals of masculinity on Over The Top (OTT) platforms in India, which have seen a marked rise in viewership since the beginning of the COVID-19 pandemic. Netflix, currently the largest investor in original content in India, offers a range of curated material in 31 languages. Masculinity over the years in Indian cinema, particularly the Bollywood industry, has largely been portrayed through hegemonic notions of normativity. Through simple distinctions between good and bad, a clear contrast is set up between a hero and an anti-hero. R. W. Connell suggests a hierarchy of masculinities, in the middle of which lies Marginalised Masculinity. This study constructs this marginalised masculinity as working-class. We contend that at the intersection of class and socioeconomic status lies a violent expression of masculinity accepted by the neo-liberal audience of Netflix. The Netflix original productions considered for this study exempt the working-class man from ethical obligations, in his conquest to liberate himself from his class position. Aspirational capitalism makes it okay to be ethically dubious, spawning a troubling masculinity. We use a three-step coding method to substantiate qualitative discourse analysis of Jamtara - Sabka Number Aayega (2020, Soumendra Padhi), Class (2023, Ashim Ahluwalia), and The White Tiger (2021, Ramin Bahrani).

Abstract

The swift advancement of vehicular edge computing (VEC) has made task offloading to roadside units (RSUs) an essential solution for addressing the computational demands of modern vehicular applications. However, efficiently partitioning and scheduling tasks across RSUs in dynamic environments remains a significant challenge. Traditional static or heuristic-based scheduling methods often struggle to adapt to dynamic vehicular networks, which leads to reduced resource utilization, increased latency, and reduced task completion rates, particularly in high-density scenarios. In this thesis, we explore the benefits of partitioned scheduling for computation offloading from multiple vehicles to RSUs, focusing on two key strategies: Fully Partitioned Scheduling (FP-Sched) and Hybrid Partitioned Scheduling (HP-Sched). These strategies are designed to handle the challenges posed by vehicle flow constraints, edge resource limitations, and bandwidth schedulability. By leveraging realworld vehicular datasets and simulations, we demonstrate that the proposed algorithms significantly improve task completion rates and reduce latency compared to traditional approaches. To further increase the adaptability and schedulability of vehicular tasks, we introduce HAPTA (Horizontal, Adaptive Learning-based, Partitioned, Timeslot-based Algorithm), a novel framework that leverages the Upper Confidence Bound (UCB) algorithm. HAPTA dynamically selects RSUs with optimal resource availability while considering vehicular mobility, task deadlines, and stringent latency constraints. The framework supports horizontal task partitioning, enabling the splitting of tasks into subtasks for efficient resource utilization across multiple RSUs. Experimental evaluations demonstrate that HAPTA outperforms existing heuristic-based methods, achieving up to 96.5% task completion in high-density scenarios while maintaining a substantial speedup over optimal scheduling frameworks. With the growing adoption of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication technologies, the proposed framework provides a robust solution for distributed computation offloading in next-generation intelligent transportation systems. This work contributes to the advancement of VEC by offering a scalable, adaptive, and efficient approach to task scheduling, paving the way for seamless integration of autonomous and connected vehicles in smart urban environments.

Abstract

Understanding the complex relationship between brain Structural Connectivity (SC) and Functional Connectivity (FC) remains a central challenge in computational neuroscience. A popular class of approaches models this relationship by simulating diffusion processes on the structural connectome to approximate functional patterns. While prior works have explored such models using fixed or multi-scale Graph Diffusion Kernels, they typically lack spatial specificity, as they do not associate diffusion parameters with individual brain Regions of Interest (RoIs). This limits the biological interpretability and precision of the models. In this work, we introduce a novel framework based on Graph Diffusion Wavelets, which enable learning region-specific diffusion scales to more accurately capture the mapping from SC to FC. By leveraging the multi-scale and localized nature of diffusion wavelets, our method uncovers how spatial communication varies across brain regions. Evaluated on the Human Connectome Project (HCP) dataset, our model achieves a high average Pearson correlation coefficient of 0.833 between predicted and empirical FC, outperforming several existing state-of-the-art methods. Importantly, the proposed architecture is linear and computationally efficient, and it reveals that the distribution of diffusion scales follows a power-law, indicative of scale-free dynamics. This is consistent with known organizational principles of the brain. Notably, we find that the bilateral frontal pole exhibits the largest diffusion scales, suggesting its integrative role within large-scale Resting-State Networks (RSNs). These findings align with previous literature on the frontal pole’s role in high-level cognitive functions. Overall, our results highlight the potential of graph diffusion wavelets as both a predictive and interpretable tool for studying brain structure-function coupling. The thesis also includes application of spectral graph theoretic method to relate the structural features like curvature to physiological traits like age and sex. Finally, the thesis is concluded by summarizing and proposing research directions.

Abstract

As machine learning models are increasingly adopted in critical decision-making domains, ensuring their fairness has become a central concern—especially when these systems have the option to abstain from making a prediction. While abstain option classifiers reduce the risk of error by deferring uncertain decisions, they may inadvertently introduce new forms of unfairness, particularly when abstention rates differ across sensitive groups. This thesis investigates the fairness implications of abstain option classification and proposes methods to mitigate associated disparities. We present EQUISCALE, a unified framework for training fair cost-based abstain classifiers under group fairness constraints. Unlike prior work that focuses exclusively on coverage-based models and single fairness criteria, EQUISCALE introduces fairness-aware loss formulations that extend demographic parity (independence) and equalized odds (separation) to the abstention setting. The approach uses Lagrangian dual optimization to enforce fairness constraints during model training. EQUISCALE is the first method to incorporate multiple fairness criteria simultaneously in abstain classification and to apply the separation criterion in this context. We validate our approach through extensive experiments on five benchmark datasets, showing substantial reductions in fairness violations without compromising classification performance. This work advances the understanding of fair abstention and provides practical tools for deploying equitable machine learning systems in high-risk environments.

Abstract

The profound immersion offered by natural walking in Virtual Reality (VR) is frequently constrained by the limited physical space available to users, hindering the exploration of expansive virtual environments. While various locomotion techniques aim to address this ”room-scale constraint,” they often introduce compromises such as reduced presence, simulator sickness, or an inability to seamlessly revisit previously explored areas. This fundamental mismatch between virtual aspiration and physical reality necessitates innovative solutions that extend navigable space without sacrificing user comfort or experiential quality. This thesis first conceptualizes the Architecturally Consistent Maze Generation for Virtual Reality (ACMGVR) framework, a novel design for the procedural generation of potentially infinite, multipath maze environments. ACMGVR prioritizes strict local architectural consistency (e.g., right-angled corridors) and supports continuous, multi-directional natural walking, including backtracking. This framework explores dynamic spatial assessment techniques to intelligently reuse physical space, laying the theoretical groundwork for creating vast, explorable worlds within confined footprints. Building upon these principles, we developed, implemented, and empirically evaluated the Procedural Overlapping Maze System (POMS). POMS employs a collision-driven, node-based algorithm to dynamically generate architecturally consistent maze sections, facilitating continuous natural walking within a reused physical footprint. A rigorous user study (N=34) comparing POMS against a spatially equivalent static maze demonstrated that POMS successfully maintained user experience (UEQ) and presence (iPQ) at levels comparable to the static condition. More significantly, participants navigating POMS experienced markedly lower increases in key cybersickness symptoms—Oculomotor, Disorientation, and Total SSQ scores—a phenomenon termed ”The POMS Effect.” This research successfully developed an innovative conceptual framework for overlapping virtual environments, translated it into a functional system, and conducted rigorous empirical validation. The findings confirm that strategically designed, dynamically overlapping architectures can not only extend perceived navigable space for natural walking in VR but also substantially enhance user comfort by reducing cybersickness, without compromising core experiential qualities. This work thus offers a validated pathway towards creating more immersive, accessible, and sustainable virtual reality experiences.

Abstract

This thesis advances the computational understanding of humor by building on the success of ensemble transformer models for Spanish humor classification, as demonstrated in our prior work at HAHA@IberLEF2021. In that study, we achieved state-of-the-art results (F1: 0.8850) in binary humor detection through ensembles of multilingual BERT, BETO, and auxiliary classifiers, while also securing competitive rankings in regression and multi-label tasks. However, humor’s subjective nature—often teetering between wit and offense—poses ethical risks for AI systems, as evidenced by high-profile failures like Microsoft’s Tay and Meta’s BlenderBot, which inadvertently generated harmful content. Here, we bridge technical innovation with ethical scrutiny. First, we extend our ensemble methodology, analyzing how transformer architectures capture linguistic nuances (e.g., irony, wordplay) in Spanish tweets while highlighting cultural biases in training data that may conflate humor with insults. We then propose strategies to mitigate such risks, including hybrid moderation systems that combine ensemble predictions with rule-based filters, and stress-test our models on edge cases where humor overlaps with offensive language. By linking robust classification to ethical safeguards, this work not only refines humor detection accuracy but also advocates for AI systems that balance creativity with cultural sensitivity. Ultimately, we argue that computational humor analysis must evolve beyond performance metrics to address the societal implications of misclassification, ensuring AI respects the delicate line between laughter and harm.

Abstract

This thesis is situated in the intersection of History and Natural Language Processing, and examines the historical transformation of trade and pastoralism in the Western Himalayas, while also introducing PastPaths, a domain-adapted Natural Language Processing (NLP) pipeline developed to extract and visualize historical trade networks from unstructured textual archives. The Western Himalayan region, including present-day Kashmir, Ladakh, Himachal Pradesh, and parts of Tibet, has historically functioned as a dense zone of exchange, sustained by the movements of pastoralist communities and transregional trade in commodities such as wool, salt, and grain. Existing historiography has primarily framed the twentieth-century decline of pastoralism as a top-down consequence of colonial policies, often sidelining the agency of local communities. Through a close reading of archival records, census data, and ethnographic literature, this thesis offers a more nuanced account by highlighting how pastoralists strategically adapted to shifting political and economic contexts. These transitions included movement into agriculture, wage labor, and state employment. From a computational perspective, the thesis addresses limitations in historical methodology by developing PastPaths, a pipeline that integrates OCR, Named Entity Recognition (NER), relation extraction, and map generation. The pipeline uses transformer-based models fine-tuned on manually annotated data from the region and is capable of extracting structured information about the trade of commodities. Our results show improved performance over baseline models and demonstrate the feasibility of large-scale trade reconstruction in data-scarce settings. The thesis contributes to both historical and computational research. It reframes dominant narratives about pastoral decline by recovering voices of adaptation and choice, while also showcasing how domain-specific NLP tools can meaningfully support historical inquiry. More broadly, it advances the field of computational humanities by proposing an integrative workflow where historical questions guide model design, and computational outputs inform previous scholarship in the human sciences. This approach offers a framework for bridging disciplinary silos and generating new insights from underexplored textual archives.

Abstract

With major advances in the field of Machine Learning, fully autonomous vehicles are becoming a reality. However, most research towards ML-based approaches for autonomous driving and safety measures so far solely focuses on four-wheelers. Two-wheelers are a primary mode of transport in many developing countries and are also very vulnerable in road accidents due to their open nature. Hence, effective safety measures for two-wheelers can potentially save millions of lives. In this thesis, we first look at the problem of driving event recognition, which is the primary step towards incorporating prediction-based models in safety features. We note that there is no public dataset that we can use as a standard for this task, and hence design a hardware system to collect data. The hardware system is modular and can be attached to any vehicle to collect six-axis acceleration and velocity data at a high polling rate. We tested traditional Machine Learning (ML) models with the annotated dataset for accuracy and find them to be insufficient due to the temporal nature of the data. So, we propose employing Deep Learning (DL) models that are better suited to processing time-series data, such as LSTMs. We also integrate the attention mechanism with the LSTM models to improve their accuracy and performance on long sequences. A comprehensive evaluation of the proposed models reveal that the Bi-LSTM model with attention provides the best accuracy. However, we also investigated the viability of these models by quantizing and deploying them on a Raspberry Pi. Here, the regular LSTM model provides the best efficiency and inference time. However, all the models are viable and successfully run on a resource-constrained platform, thus indicating that there is scope for deployment of predictive models with inference on edge. Next, we introduce a scalable and modular platform for comprehensive two-wheeler riding data collection. The platform is equipped with multiple sensors and high-performance embedded systems, and captures essential data points such as GPS, acceleration, gyroscope, speed, and 360-degree image and depth data which are crucial for developing deep learning models for autonomous navigation and accident prevention. We detail the hardware architecture, consisting of an Nvidia Jetson TX2 NX platform, Raspberry Pi 4 Model B, and various sensors, all tailored to operate efficiently on a two-wheeler. The software methodology is designed to synchronize and process the data effectively, hence allowing us to generate accurate and thorough datasets. Our results demonstrate the potential of the platform towards improving two-wheeler safety and contributing to the advancement of autonomous vehicle technologies. Finally, we provide an analysis of the shortcomings of the platform architecture that we observe after the first few data collection runs, and we detail the fixes that we implement. We also discuss the scope of this work and future work that can be done in the domain of two-wheeler data collection, as well as the deployment of predictive models on two-wheelers for various purposes. Overall, this thesis aims to advance the integration of machine learning and predictive models for two-wheeler safety by addressing critical gaps in data availability, model design, and edge deployment. We demonstrate the potential of lightweight model inference on edge and also develop a novel architecture of comprehensive riding data collection, which is essential for large-scale model training.