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

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.

Abstract

This thesis is based on a research problem carried out in [1]. The problem statement deals with the identification of secret sharable state in three qubit system for which maximum secret reconstruction is possible. We classify this set of states as MSR states. For a given value of maximum of the tele- portation fidelities that can be achieved by the bipartite channels of dealer-receiver and dealer-assistant, these states achieve the maximal secret reconstruction fidelity. In other words, it deals with finding a potential resource for secret sharing that maximizes the potential for secret sharing. A tripartite state can be considered a resource for secret sharing if it imposes limits on the teleportation fidelities in both bipartitions involving the dealer while still being useful to reconstruct the secret. Similarly, for a specific maximum value of the Bell-CHSH measurement in the bipartitions specified above, we determine the highest possible reconstruction fidelity. Another interesting discovery is that all secret-sharable states satisfy Bell’s inequality in both dealer- reconstructor and dealer-assistant channels. This reveals a novel form of mutual exclusivity between secret-sharable states and violations of Bell’s inequality. This thesis is based on our findings in [1] and it helps to identify the best candidates among secret sharing resource states to achieve maximum recon- struction fidelity, setting a practical limit on information transfer in a resource theoretic extension of secret sharing. Moreover, it highlights a new form of exclusivity between bipartite correlations and the ability to perform secret sharing in a tripartite setting

Abstract

In the rapidly evolving landscape of Very-Large-Scale Integration (VLSI) design, the integration of machine learning (ML) techniques has emerged as a powerful tool to enhance automation and optimization processes. However, the effectiveness of these ML applications is often hampered by a critical challenge: data scarcity. As VLSI systems grow in complexity, the demand for high-quality training datasets becomes increasingly vital for the development of robust and accurate ML models. This thesis addresses this pressing issue by exploring innovative strategies for data augmentation through the application of generative models, specifically Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models. The initial segment of this research investigates the use of GANs to generate supplementary circuit data based on simulations performed in established design environments, including Cadence Virtuoso, HSPICE, and Microcap. By leveraging GANs, we aim to synthesize artificial circuit data that accurately reflects the characteristics of real-world data, thereby enhancing the training sets available for machine learning algorithms. A comprehensive comparative analysis is conducted to evaluate the performance of GANs against VAEs, highlighting their respective strengths and weaknesses in the context of data augmentation specifically tailored for VLSI applications. Subsequently, the thesis delves into the capabilities of diffusion models, which have recently gained prominence for their effectiveness in generating high-fidelity synthetic data. By utilizing these advanced generative techniques, we demonstrate the potential to produce artificial datasets that closely resemble real electronic circuit behavior, effectively addressing the limitations posed by traditional data collection methods. Through simulations conducted in the HSPICE design environment, we establish the quality and reliability of the synthetic data generated, thereby validating its applicability in enhancing ML model performance. A key focus of this research is the rigorous evaluation of the authenticity of the synthetic data and its impact on the predictive accuracy of machine learning models. The results reveal a significant reduction in prediction errors for circuit performance assessments when models are trained on augmented datasets, showcasing the transformative potential of generative models in the VLSI design domain. This improvement in accuracy not only highlights the effectiveness of the proposed methodologies but also underscores the importance of data-driven approaches in advancing the capabilities of electronic design automation In addition to presenting the main contributions of this thesis, this work also sheds light on the challenges encountered during the implementation of generative techniques. Issues such as the calibration of model parameters, the validation of synthetic data fidelity, and the integration of these data solutions into existing design workflows are critically examined. Furthermore, we provide insights into the lessons learned from experiments that did not yield successful results, offering valuable perspectives for future research endeavors in this field. By addressing the data scarcity issue through innovative generative modeling approaches, this thesis contributes to the ongoing dialogue within the VLSI community regarding the future of machine learning applications in electronic design automation. The findings of this research not only pave the way for more effective ML solutions in VLSI design but also serve as a foundation for future advancements that may ultimately reshape the landscape of electronic design and technology