Abstract

Intermittent Water Supply (IWS) systems, prevalent in many developing regions, pose significant challenges for urban water management due to their scheduled delivery and the resulting need for consumers to store water for non-supply periods. The lack of high-frequency, building-level consumption data in such systems hinders accurate demand profiling, leakage detection, and informed infrastructure planning. This thesis addresses these gaps by developing and deploying scalable, Internet of Things (IoT) enabled strategies to capture and analyse water consumption patterns in IWS settings, with a particular focus on the Indian context. The work is structured in two parts: first, improving the accuracy and reliability of IoT-based smart retrofit devices for analog water meter digitization; and second, constructing and analysing water consumption curves at different temporal resolutions for diverse building types using the collected data. Retrofitting existing analog water meters is a cost-effective approach to enable high-frequency, automated water usage monitoring without replacing established infrastructure. While image-based meter reading using IoT devices has shown promise, real-world deployments encounter significant challenges such as dew accumulation, scratches, smudges, and insect intrusion, all of which can degrade image quality and lead to digit detection errors. To address these issues, this thesis introduces a lightweight Hamming distance-based refinement algorithm that systematically corrects digit misclassifications by leveraging the expected range of meter readings and minimizing the number of differing digits. The algorithm is computationally efficient, requiring only basic arithmetic operations, and is thus well-suited for real-time deployment on edge devices like the Raspberry Pi. To further enhance data quality, a webbased annotation tool was developed, enabling rapid, targeted manual validation of flagged anomalies and supporting the efficient correction and curation of large datasets. Building on these advancements, the thesis proposes two innovative, non-intrusive IoT-based strategies for capturing water consumption in IWS-serviced buildings. The first strategy involves installing smart flow meters at the outlets of Over Head Tank (OHT) to directly measure consumption. The second combines flow meters at the inlet with water level sensors inside OHTs, using mass conservation principles to infer consumption at high temporal resolution. These approaches minimize the need for intrusive instrumentation and are readily scalable to a variety of building types commonly found in Indian urban settings. A comprehensive field study was conducted at International Institute of Information TechnologyHyderabad (IIIT-H), where seven IoT nodes were deployed across five buildings, including hostels and classroom blocks. Over 125,000 data points were collected at three-minute intervals over a period of two months, capturing detailed water usage dynamics. The data underwent rigorous cleaning, refinement, and synchronization, leveraging the developed algorithms and annotation tools to ensure high integrity and accuracy. Using a bottom-up approach, the thesis constructs instantaneous, diurnal, and weekly water consumption curves for each building, normalized using peak factors to facilitate comparison of demand volatility across user categories. The resulting consumption patterns reveal distinct signatures for residential and non-residential buildings, highlight the impact of scheduled events (such as sports screenings and hackathons), and quantify temporal variations in demand. These high-resolution insights enable utilities and building managers to detect leaks and unauthorized usage, optimize supply schedules, and inform the design of future-ready water distribution networks. The methodologies and findings also provide a robust foundation for transitioning from IWS to CWS systems and for upgrading conventional buildings to smart, resource-efficient infrastructure.

Abstract

The digital age has amplified the importance of inclusive and comprehensive information dissemination, yet many low-resource languages remain underrepresented in this sphere. The lack of advanced natural language processing (NLP) tools for these languages hampers the ability to effectively combat misinformation, detect abusive content, and provide enriched informational resources. Addressing these challenges is crucial for fostering digital inclusivity and ensuring that speakers of low-resource languages have equitable access to reliable and enriched information. Our first study focuses on detecting fake news in Dravidian languages by leveraging Google’s MuRIL (Multilingual Representations for Indian Languages) model. By fine-tuning the ”MuRIL-base-cased” variant with a carefully curated dataset of labeled comments and posts in Dravidian languages, we enhance the model’s ability to differentiate between authentic and deceptive content. The performance evaluation using standard metrics demonstrates the effectiveness of MuRIL in maintaining a safer digital ecosystem for speakers of Dravidian languages. In the second study, we address the pressing need for detecting abusive language in multimodal content, particularly in Tamil videos, and performing sentiment analysis for Tamil and Malayalam videos. Our novel framework integrates state-of-the-art models such as Multiscale Vision Transformers (MViT) for video analysis, OpenL3 for audio analysis, and bert-base-multilingual-cased for textual analysis. By combining these multimodal features, our approach significantly improves the accuracy of abusive content detection and sentiment categorization, contributing to a safer and more inclusive online environment for Dravidian language speakers. The third study tackles the challenge of information scarcity in the very low-resource language of Mizo. We investigate a methodology to generate holistic summaries of Mizo news articles, enriched with information from English-language news to enhance coverage. By providing a dataset of 500 Mizo news articles with corresponding enriched summaries, our approach significantly improves the comprehensiveness and accessibility of news content for Mizo speakers. Human evaluations confirm the effectiveness of our method in meeting the information needs of this community. Together, these studies highlight the critical importance of developing advanced NLP techniques for underrepresented languages. By addressing fake news detection, abusive language identification, and information enrichment in Dravidian and Mizo languages, our research makes significant strides toward digital inclusivity and safety, ultimately enhancing the digital experience for speakers of these languages and paving the way for future innovations in multilingual and multimodal content analysis.

Abstract

Environmental law in India has played a pivotal role in balancing economic development with ecological sustainability, with the Supreme Court emerging as a crucial actor in shaping environmental jurisprudence. However, the ways in which courts frame environmental concerns significantly influence judicial decision-making, policy formulation, and public discourse. This thesis investigates the legal narratives surrounding environmental issues in Indian judicial decisions, analyzing how courts construct arguments around environmental protection, economic growth, and social justice. By applying computational methods to a curated dataset of Supreme Court judgments, this research uncovers patterns in legal argumentation and broader ideological shifts within environmental jurisprudence. While traditional legal scholarship has extensively examined the doctrinal foundations of environmental law, there remains a significant gap in understanding the rhetorical and structural aspects of judicial decision-making. This study addresses this gap by adopting a computational humanities approach, employing natural language processing (NLP) and statistical analysis to identify dominant themes and argumentative strategies in environmental case law. Through topic modeling and citation analysis, this research maps the evolution of environmental jurisprudence in India, highlighting the judiciary’s role in shaping governance mechanisms and regulatory frameworks. The findings reveal critical insights into the judiciary’s framing of environmental issues. The study identifies the predominance of individual appellants in environmental litigation, suggesting a growing public engagement in environmental advocacy, but also underscores the limited participation of institutional actors such as state governments and NGOs. The thematic analysis of case topics shows that courts focus heavily on constitutional rights, pollution control, and land disputes, while areas such as biodiversity conservation and hazardous waste management receive comparatively less judicial attention. Additionally, the temporal analysis of forest rights cases demonstrates how legislative interventions, such as the Forest Rights Act (2006), directly influence litigation trends, reaffirming the judiciary’s role in shaping environmental governance. This study also examines the legal principles that form the foundation of environmental jurisprudence in India, particularly the interpretations of Articles 14, 21, and 32 of the Indian Constitution. By analyzing landmark cases, the research highlights how the Supreme Court has expanded environmental rights under Article 21’s ”Right to Life,” employed the Public Trust Doctrine, and developed principles such as ”Polluter Pays” and ”Precautionary Principle.” The increasing reliance on Public Interest Litigations (PILs) has democratized access to environmental justice but has also led to concerns about the potential misuse of this legal mechanism. The study’s methodological innovation lies in leveraging computational techniques for legal analysis. By integrating large-scale text mining with qualitative legal interpretation, this thesis offers a data-driven perspective on judicial reasoning. The citation analysis of frequently referenced cases, laws, and precedents provides insights into how legal arguments evolve over time. The application of NLP techniques enables the identification of implicit biases in judicial narratives, revealing recurring themes and the ideological underpinnings of environmental litigation. Despite its contributions, the research acknowledges certain limitations, including the reliance on a specific dataset and the challenges of drawing strong correlations between case topics and appellant types. Future research could expand this work by conducting comparative studies across different jurisdictions, exploring regional variations in environmental litigation, and incorporating qualitative insights from legal practitioners. Ultimately, this thesis contributes to a deeper understanding of how environmental concerns are framed, contested, and adjudicated in India’s judicial system. By bridging environmental law with computational humanities, this study not only enriches legal scholarship but also provides valuable insights for policymakers, environmental activists, and legal practitioners. As climate change and sustainability debates gain prominence, the role of the judiciary in environmental governance will continue to shape the trajectory of India’s environmental policies and legal frameworks

Abstract

The exponential growth of big data, driven by rapid technological advancements, has transformed the landscape of data analysis and business decision-making. The integration of data from the Internet of Things (IoT) devices, healthcare, manufacturing, banking, and other applications has opened up new avenues for deriving valuable insights and enhancing business operations. Organizations leverage powerful analytics to drive strategic decisions, identify opportunities, and improve performance. However, the vast increase in data usage and complexity brings significant security and privacy challenges. Big data relies heavily on infrastructure, cloud, and edge computing, all of which pose inherent security risks. Untrusted applications and insufficient security measures can introduce vulnerabilities into enterprise systems. Additionally, the widespread use of mobile and IoT devices increases the risk of unauthorized access and misuse of sensitive data. Enterprises must prioritize defining secure access privileges and implementing robust security protocols to prevent potential data misuse. As data sharing and source authentication become more prevalent, trust between parties remains a critical issue. Governments, for instance, are cautious about sharing data due to national security concerns, while individuals worry about the privacy of their personal information. The involvement of multiple stakeholders often leads to poor control over data transfer, hindering the development of the big data industry. The presence of personally identifiable information (PII), trade secrets, and other sensitive details raises significant privacy concerns. Balancing the utility of data analysis with the protection of individual privacy requires employing various cryptographic, anonymization, and access control methods. Compliance with privacy regulations, such as the “General Data Protection Regulation (GDPR) in the European Union”, is crucial for organizations handling big data. Non-compliance can lead to severe legal and financial consequences. Therefore, secure big data analytics requires secure data transfer and privacy-preserving that necessitates a combination of technical, organizational, and legal measures to ensure that individual privacy is protected while still enabling the derivation of valuable insights from large-scale data analysis. Thus, the ongoing challenge of secure big data analytics demands innovative solutions that can effectively secure data with privacy protection. Existing methods often struggle with complexity, scalability, and the ability to ensure secure big data analytics while maintaining privacy and confidentiality of data. Organizations must continuously adapt and enhance their strategies to keep pace with the evolving landscape of big data and its associated risks. The first study in this thesis presents a comprehensive approach that utilizes data of smart devices in IoT. This data is crucial for building intelligent applications using machine learning.Typically, data from these devices is collected into data centers for training machine learning models. The traditional centralized training approach, which requires transferring data from devices to a central server, is inefficient due to privacy concerns and bandwidth limitations, as users are often reluctant to share their data with centralized data centers. To address these issues, we propose an efficient hybrid secure federated learning approach integrated with blockchain technology. This method allows models to be securely trained locally on devices, with the model and its parameters stored in the blockchain for traceability and immutability. A detailed security and performance analysis demonstrates the efficacy of this proposed approach, highlighting its security, resilience against numerous attacks, and cost-effectiveness in computation and communication compared to existing schemes. We introduce a secured federated learning approach for big data analytics based on an authenticated key agreement protocol and blockchain. We utilize the “Genetic Algorithm Neural Network (GANN)” to train our model and propose a secure and efficient authentication and key agreement scheme to ensure the secure exchange of model parameters in federated learning. The transactions created as model parameters among IoT smart devices are securely stored in blocks and added to the blockchain using the “Practical Byzantine Fault Tolerance (PBFT)” consensus protocol. Experiments and performance analysis of our proposed machine learning model indicate its efficiency under various attack scenarios. Additionally, a detailed formal security analysis and verification using the widely-used Scyther simulation tool show that the proposed scheme is resilient against several attacks common in the IoT environment. A rigorous comparative study confirms that the proposed scheme offers an accurate, secure, and resilient federated ma

Abstract

Experiments at the Large Hadron Collider (LHC) aim to discover exotic particles by colliding protons at extremely high speeds and analyzing the resulting data. A key challenge in these processes is distinguishing between experimental observations and theoretical predictions, particularly in separating signals of interest from background noise. Traditionally, cut-based methods have been employed for this purpose; however, while effective to some extent, these methods are often inefficient for optimal signal-background classification. In this thesis, we explore various machine learning-based approaches to improve signal-background classification, leveraging their potential to outperform traditional techniques. Additionally, we investigate a phenomenological search for a Z ′ boson decaying into right-handed neutrinos, utilizing these advanced methods to achieve significantly better classification accuracy compared to earlier cut-based analyses. This work highlights the transformative role of machine learning in enhancing the sensitivity and efficiency of particle physics research.

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