Abstract

Toxicity in online gaming is more than just an unfortunate side effect of competitive play; it is a deeply embedded issue shaped by game design, player dynamics, and broader social structures. While research on gaming toxicity has largely centred on Western contexts, this study shifts focus to India, where unique cultural, linguistic, and regional factors influence how toxicity is experienced and perpetuated. Gaming in India is a rapidly growing space, yet it remains deeply exclusionary in many ways, with toxicity often targeting players based on gender, language, region, or religious identity. Through an ethnographic approach, including in-depth interviews and observational co-playing, this study investigates how toxicity manifests, how players rationalise or resist it, and how online interactions reflect broader societal divisions. One of the key concerns explored in this research is the role of game design and matchmaking systems in reinforcing bias and exclusion. Most matchmaking algorithms prioritise skill-based ranking and latency optimisation but fail to consider the social dimensions of player experiences. This oversight results in structures that inadvertently foster hostility, as players are matched without consideration for linguistic compatibility, cultural norms, or pre-existing biases. The study reveals that players from marginalised groups, such as women, non-binary players, and linguistic minorities, frequently encounter discrimination and harassment, often leading them to modify their behaviour, hide aspects of their identity, or disengage from competitive gaming entirely. The findings also highlight the normalisation of toxicity in online spaces, where aggressive behaviour is often framed as a natural or even necessary part of competition. In Indian gaming communities, toxicity is deeply gendered, with female players regularly facing scepticism, harassment, and pressure to prove their competence. Additionally, linguistic and regional biases shape in-game interactions, where players who speak certain languages or exhibit accents associated with particular regions are frequently targeted. The study also examines how asymmetric and symmetric game structures influence the dynamics of toxicity, with different formats fostering distinct patterns of aggression and exclusion. Beyond player interactions, this study considers the impact of external influences, including streamers, gaming influencers, and community echo chambers, in legitimising or amplifying toxic behaviour. Popular streamers often set behavioural norms for younger audiences, reinforcing toxic patterns that trickle down into everyday gaming culture. While some gaming personalities attempt to promote inclusivity, others actively foster hostility, normalising aggression and harassment in ways that extend beyond individual matches or specific game titles.Rather than relying solely on reactive measures like reporting and banning, there is a need to integrate proactive design changes, such as improved matchmaking filters, greater support for linguistic diversity, and better moderation tools, that take into account the cultural and social realities of gaming in the Global South. This study is also grounded in a postcolonial perspective, recognising that global gaming platforms often reflect Western norms, values, and modes of communication that do not always align with the lived experiences of players in regions like India. The dominance of English, the lack of region-sensitive moderation, and the design of social systems in games often position Indian players at the margins, forcing them to adapt to structures that were never made with their context in mind. Toxicity, in this sense, is not only a behavioural issue but also a symptom of deeper structural inequalities rooted in global digital hierarchies. By situating Indian players within this broader postcolonial framework, this research highlights how digital spaces continue to reproduce older power dynamics, demanding more localised and culturally sensitive approaches to gaming design and governance. This study contributes to critical discussions on gaming toxicity, algorithmic bias, and social disparities in digital environments, offering valuable insights for researchers, game developers, and policymakers. As online gaming continues to grow in India and other non-Western contexts, understanding how toxicity operates in these spaces is crucial for fostering more inclusive and equitable multiplayer ecosystems.

Abstract

We examine the mutual exclusion problem of concurrency through the systematic application of modern feedback control theory, by revisiting three classical problems involving mutual exclusion: Generalised Dining Philosophers, Peterson’s Mutual Exclusion and fine-grained mutation of concurrent data structures. The result is a modular development of the solution using the notions of system and system composition in a formal setting that employs simple equational reasoning. In each case, solving the problem reduces to designing an appropriate feedback controller. The modular approach separates the solution architecture from the algorithmic minutiae and has the benefit of simplifying the design and correctness proofs.

Abstract

Ink-bleed is a common form of degradation in handwritten document images, where ink from the reverse side or adjacent lines seeps into the visible content, impairing readability and adversely affecting downstream tasks such as Optical Character Recognition (OCR). Traditional image processing techniques often fall short in preserving the fine structure of handwritten strokes while removing bleedthrough noise. This thesis presents a machine learning-based computational framework for ink bleed suppression using a layer separation approach. The proposed method models the document image as a combination of content and bleed-through layers and employs a Dual Layer Markov Random Field (DL-MRF) architecture to learn the separation in a supervised setting. A synthetic dataset with controlled bleed artifacts was constructed for training, along with augmentation strategies to simulate various bleed intensities and patterns. The model was evaluated on both synthetic and real handwritten document images. The results demonstrate significant improvement over traditional filtering techniques and baseline learning models, preserving content integrity while effectively reducing ink bleed. This work contributes towards robust document image restoration, with applications in digital archiving, historical manuscript preservation, and pre-processing pipelines for handwriting analysis systems.

Abstract

This thesis examines the interplay of “two-sided gamification” in India’s fast-growing on-demand food delivery industry. While platforms such as Zomato and Swiggy use algorithmic management and game-like incentives to standardize gig work, delivery agents respond with creative, sometimes subversive strategies of their own—what we term “counter-gamification.” Drawing on extensive fieldwork across Mumbai, Hyderabad, Chennai, and Bengaluru over two years, the research unpacks how algorithmic frameworks for order allocation, performance metrics, and tiered benefits intersect with workers’ lived realities. On one hand, platforms promise flexibility and straightforward entry into waged employment for populations often excluded from formal labor markets. On the other hand, they subject workers to opaque policies, unpredictable shifts in incentives, and limited recourse against unfair practices. Through interviews and broader quantitative trends from 76 engaged participants, this study illuminates the tension between top-down control and everyday acts of worker agency, including selective order acceptance, “gaming” performance metrics, and peer-based support networks. The findings highlight the need for nuanced policy interventions and system designs that support fair labor practices, while also foregrounding the distinct ways agents in the Global South negotiate and reshape platform-driven labor structures.

Abstract

Engaging diverse participants in meaningful online political discussions remains a crucial challenge for contemporary democratic societies. Two central barriers to vibrant political discourse online are cross-partisan communication divides, where opposing ideological groups struggle to interact constructively, and the limited participation from politically disinclined individuals (PDIs), who often perceive themselves as lacking sufficient political knowledge or fear negative social consequences. Although existing literature presents mixed findings regard- ing the efficacy of personal narratives in political communication, this thesis investigates the potential of personal narratives—defined as short, first-person accounts emphasizing personal experiences—to bridge these divides and increase participation. Experimental research has shown that personal narratives can foster empathy, respect, and trust across ideological boundaries by enabling identification and emotional engagement. Con- versely, some studies suggest that personal narratives in social media environments can inad- vertently reinforce in-group solidarity, strengthen partisan identities, and amplify polarization due to platform-specific affordances, such as quantifiable feedback mechanisms. To resolve these discrepancies and comprehensively evaluate the role of personal narratives in real-world settings, this research conducts a large-scale computational analysis of political discussions on Reddit. Utilizing a BERT-based text classifier to identify personal narratives, alongside computa- tional methods to infer user ideology and participation patterns, the analysis reveals encouraging results. First, politically disinclined individuals are significantly more likely to contribute per- sonal narratives in political communities compared to their politically active counterparts, and such narratives notably increase their continued engagement over time. Additionally, personal narratives posted by PDIs consistently receive more positive feedback from community mem- bers relative to their non-narrative contributions, suggesting their unique value in enhancing inclusive discourse. Furthermore, when examining cross-partisan interactions, personal narratives exhibit varied impacts: notably, right-leaning individuals using personal narratives receive more favourable feedback from left-leaning communities, indicative of reduced partisan animosity and increased openness toward opposing viewpoints. However, this positive effect is asymmetrical, as similar outcomes are not consistently observed for left-leaning individuals. Additionally, while personal narratives alone do not substantially increase the probability of replies across ideological di-vides, they do significantly mitigate hostility levels, fostering a more respectful cross-partisan environment. This thesis argues that personal narratives not only empower politically disinclined individ- uals by lowering barriers to participation but also partially alleviate polarization by fostering empathy and respect in cross-partisan settings. These findings highlight personal narratives as valuable tools for enhancing democratic engagement. Future research could further explore the conditions under which personal narratives exert their strongest positive effects, as well as examine methods to systematically encourage the use of inclusive and empathetic storytelling within contentious political environments online.

Abstract

Time series can model many phenomena of interest. Additionally, with the advent of smart cities, we have sensors all around us measuring changing quantities, such as air quality, weather patterns, and traffic dynamics. Also, people regularly monitor their health indicators using fitness trackers. There exist several applications consisting of such multi-variate time series. Missing values are ubiquitous in these time series due to multiple reasons. Sensors can stop working over time due to environmental exposure, resulting in contiguous missing values until replaced. Momentary connectivity issues can also occur, resulting in missing values. Data storage and pre-processing errors also sometimes lead to data corruption. Privacy issues can further cause missing values. Using simple methods like interpolation can introduce significant bias. Imputation is a vital pre-processing step for any downstream time series analysis, including forecasting, classification, and anomaly detection. Inaccurate imputations can lead to biased analysis and flawed decision-making in healthcare, finance, and urban systems. Multi-variate time series imputation is a challenging problem because of the input’s temporal, spatial and space-time dependencies. For example, there can be a correlation in traffic speeds across a city’s roads over time. An accident on one road may affect the speed on a nearby road in the future. Real-world time series are additionally prone to being noisy and nonstationary. We propose two novel architectures – Product Graph U-networks using Temporal pooling for Spatiotemporal imputation (P-GUTS) and Sparse Attention-based Imputation Network for Time series (SAINT), which offer distinct advantages in terms of performance and efficiency. We are inspired to make efficient yet competitive networks due to hardware limitations. PGUTS can consider multiple data views to learn comprehensive representations, enabling it to generalize to multiple real-world datasets. SAINT is a more efficient network that is practical even for long-term forecasting. It leverages the idea of sparsity from the literature to divide the computations on the space-time graph sequentially. Both networks outperform strong baselines, are robust to increasing missing rates, and are useful for forecasting.

Abstract

Disfluencies are inherent components of fluent speech, reflecting the natural processes involved in speech planning and production. They can be categorized into typical disfluencies, such as hesitations and self-repairs, and atypical disfluencies associated with speech disorders like stuttering. From a theoretical perspective, disfluencies can be interpreted as indicators of cognitive errors or as functional elements of conversation. The cognitive view posits that disfluencies signal covert errors in language formulation, while the strategic view considers them as pragmatic tools that facilitate communication. For instance, hesitations may serve to stall for time during language formulation or to correct errors, whereas filled pauses can indicate the speaker’s need for attention or signal uncertainty in message planning. Similarly, repetitions can enhance narrative cohesion or act as stalling techniques. This thesis explores the intricate distinctions between typical and atypical speech disfluencies, emphasizing their implications for speech technology and clinical applications. Accurate classification of these disfluencies is critical for enhancing voice assistants (VAs) designed for Persons Who Stutter (PWS), as misidentification can lead to premature cutoffs during speech. Furthermore, early detection of stuttering in children is vital to prevent misdiagnosis as developmental language disfluency. To address these challenges, this research introduces the IIITH-TISA dataset, the first Indian English stammer corpus capturing atypical disfluencies, and extends the IIITH-IED dataset with detailed annotations for typical disfluencies. Employing Perceptually Enhanced Zero-Time Windowed Cepstral Coefficients (PE-ZTWCC) in conjunction with Shifted Delta Cepstra (SDC), we utilize a shallow Time Delay Neural Network (TDNN) classifier to achieve an average F1 score of 85.01% for disfluency classification, surpassing traditional feature sets. Additionally, this study leverages intermediate representations from four pre-trained self-supervised models—Wav2Vec2.0, HuBERT, WavLM, and TERA—to classify typical and atypical disfluencies within the context of two novel Indian English datasets. Classification experiments utilizing support vector machines (SVM) and convolutional neural networks (CNN) demonstrate that features extracted from HuBERT’s 5th layer yield a peak F1 score of 0.97. These results underscore the significance of intermediate layer representations in discerning subtle variations in speech patterns and contribute to the development of robust and interpretable systems for automatic speech disfluency classification. This research not only explores the understanding of speech disfluencies in a linguistically diverse context but also lays the groundwork for future innovations in speech technology that can better accommodate individuals with speech disorders.

Abstract

Coreference resolution (CR) is the task of identifying text spans that refer to the same entity. It is a fundamental component of natural language understanding with applications in various downstream NLP tasks, such as question answering, knowledge graph construction, and summarization. Despite its significance and the advancements made by neural coreference models, CR models face a major bottleneck: their limited generalization capability. Prior work attributes this generalization gap to differences in annotations, such as what constitutes a mention (or entity) and varying preferences to span boundaries. For a model to have strong referential capabilities, it must adapt to these annotation-specific nuances. However, achieving this level of adaptability remains a significant challenge, even for state-of-the-art (SOTA) models. This challenge is further amplified when evaluating the referential capabilities of large language models (LLMs) in a few-shot setting, where replicating nuanced annotations with just a few examples is highly unrealistic. We observe that these annotation-specific nuances, can be beneficial but are not essential for downstream tasks or for evaluating the core referential capabilities of an LLM. We describe these nuances as bells and whistles. In this work, we redefine the traditional formulation of coreference resolution by shifting focus away from its bells and whistles. Instead, we propose task formulations more aligned with practical applications and demonstrate improved generalizability across domains. Our first contribution introduces an alternative referential task, Major Entity Identification (MEI). MEI simplifies referential tasks by:(a) assuming that target entities are explicitly provided in the input, and (b) focusing exclusively on frequent entities. Assuming entities to be part of the input shifts the responsibility for domain-specific annotation adaptation—determining which entities are annotated—from the training phase to inference. Through extensive experiments, we show that MEI models generalize effectively across domains using both supervised approaches and LLM-based few-shot prompting across multiple datasets. Importantly, MEI aligns with the classification framework, enabling the use of robust, intuitive, and well-understood classification-based evaluation metrics. Beyond its theoretical appeal, MEI also has practical utility as it allows users to efficiently search for all mentions of a specific entity or a group of entities of interest. Our second major contribution addresses critical shortcomings identified in recent evaluations of large language models (LLMs) on coreference resolution. These studies revealed that traditional output formats and evaluation metrics fail to capture models’ referential understanding fully. Traditional evaluation methods require reproducing the entire document along with annotated cluster information or precisely replicating the antecedent span. This introduces additional bells and whistles, such as ensuring the accurate reproduction of spans and documents. To tackle this issue, we introduce IdentifyMe, a new benchmark for mention resolution that adopts a multiple-choice question (MCQ) format—a widely used evaluation approach for LLMs. With this simplified task design, any failure can now, be attributed exclusively to issues with mention resolution. IdentifyMe presents long narratives and applies heuristics to eliminate easily identifiable mentions, resulting in a more challenging and rigorous task. The benchmark incorporates a curated mix of various mention types and their corresponding entities, enabling fine-grained analysis of model performance. Notably, LLM performance remains substantially below human-level performance on IdentifyMe, highlighting considerable room for improvement even for advanced models like GPT-4. The evaluation also reveals key weaknesses in current LLMs, particularly with pronominal mentions, nested mentions, and other nuanced cases. Overall, this work moves beyond traditional coreference resolution formulations, focusing on tasks with practical applicability and providing fresh insights into the referential strengths and weaknesses of current models. We term this approach Coreference Without Bells and Whistles — a streamlined perspective that prioritizes utility and understanding of model capabilities over tailored annotation adaptation

Abstract

Sentiment analysis has emerged as one of the most sought-after research areas in recent years. With the ever-increasing volume of data generated every day, it is essential to mine this information and analyze the opinions and facts it contains, in order to build better systems that can assist us in our day-to-day activities. With over 345 million speakers1 , Hindi is the fourth most widely spoken language in the world. In the 2000s, English and European languages dominated web forums and social media. However, over the following decade, Hindi experienced rapid growth in its online presence. In 2015, according to various sources, Hindi content consumption grew by 94% year over year, compared with just 19% for English2 . This boom in Hindi usage on the web calls for opinion-mining systems that can effectively process Hindi data and build accurate text-analysis models. Hindi is morphologically rich and has free word order, unlike English. Performing sentiment analysis on a resource-scarce language therefore remains a challenging task. Our work focuses on overcoming these challenges and creating an efficient system that can be easily scaled to other Indian languages for polarity determination. As part of this effort, we generate a Sentiment Treebank that provides word-level polarity along with other morphological features and a sentence-level score derived from heuristics. The Treebank contains over 10,000 sentences from various domains, each annotated with both word- and sentence-level polarity scores. In addition, we introduce a manually created corpus of 3,500 Hindi sentences mined from news blogs, each labeled with sentence-level polarity markers. Beyond resource creation, we perform sentence-level classification on the annotated corpus using three approaches: • Supervised approach using morpho-sentiment features. • Karaka-based approach, in which the thematic relations of the main verb and its participants are leveraged to generate patterns for cross-lingual sentiment indexing. • Shallow-parser approach, which extracts noun and adjective chunks to form phrases. Opinion scores from a resource-rich language (Language_1) with a well-developed sentiment lexicon are projected onto Hindi through phrase-level translation, thereby minimizing polarity-transfer errors. A key advantage of this method is its independence from domain-specific labeled data. This cross-lingual framework minimizes dependence on the source language and exploits sentiment scores in the source lexicon to assign scores to the target language. We assume that the presence of an opinion-bearing word in a chunk polarizes the entire chunk, allowing us to aggregate chunk-level sentiments into a sentence-level label. Finally, we analyze the merits and drawbacks of these systems, the impact of language representation on polarity determination, and the challenges inherent in processing user-generated Hindi text for opinion labeling.

Abstract

This thesis presents novel contributions to the field of Private Information Retrieval (PIR), a fundamental cryptographic technique that enables users to query public databases without revealing the identity of the accessed data. The study begins by revisiting classical PIR schemes introduced by Chor et al. [1], establishing a foundational understanding of how user privacy is preserved through query obfuscation. Building on this foundation, the thesis advances PIR design by incorporating modern dis- tributed storage systems and powerful coding-theoretic tools, focusing on the application of Berman codes—binary linear codes of length nm (for n ≥ 2, m ≥ 1)—which are known to achieve the capacity of the binary erasure channel. A significant portion of the work explores (D, E)-retrieval schemes, which utilize linear codes for both storage and retrieval, enabling ro- bustness against server collusion and improving data access efficiency. A key contribution of this thesis is the development of a new PIR framework that supports up to t colluding servers. This construction employs the star product of Berman codes and achieves strong privacy guarantees while maintaining optimality in terms of PIR capacity. The proposed schemes provide flexible control over system parameters, including the number of servers, the storage overhead, the PIR rate, and the collusion threshold t. Our results introduce a new class of capacity-achieving PIR protocols that ensure the privacy of the retrieved file’s identity, even in adversarial settings with colluding databases. Notably, these schemes are constructed entirely over binary fields, offering a practical advantage by reducing implementation complexity compared to prior approaches that require large finite fields. Through rigorous theoretical analysis and constructive design, this work demonstrates the prac- tical viability and theoretical strength of Berman-code-based PIR systems. The methodologies proposed herein significantly enhance the privacy, efficiency, and deployability of PIR schemes, with far-reaching implications for secure data access in privacy-sensitive domains such as health- care, finance, and cloud computing.