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

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

Abstract

The emergence of AI-driven systems has transformed smart grid networks, enabling widespread automation in decision-making. A smart grid is an advanced electricity distribution system that empowers customers to actively participate through smart meters. These grids operate across three key markets: wholesale, tariff, and balancing markets. In this ecosystem, distribution companies, or electricity brokers, play a pivotal role by procuring electricity in the wholesale market through double auctions and selling it to customers in the tariff market. Meanwhile, the balancing market oversees real-time supply and demand, penalizing brokers responsible for imbalances. The primary goal of brokers is to maximize profits, which involves addressing three critical challenges: (i) minimizing procurement costs in the wholesale market, (ii) offering competitive yet profitable tariffs in the tariff market, and (iii) mitigating peak demand scenarios to avoid penalties. Achieving this requires brokers to develop intelligent strategies leveraging AI techniques. This thesis develops AI-based strategies that enhance the efficiency of electricity brokers, tested using a close-to-real-world simulation platform, PowerTAC. Specifically, we design various bidding strategies for brokers operating in the wholesale market, where electricity is traded through day-ahead periodic double auctions (PDAs). The first strategy draws inspiration from game theory, leveraging Nash equilibrium principles for a single-buyer and single-seller setup. This is extended to real-world scenarios consisting of multiple buyers and sellers using reinforcement learning techniques. The second strategy reduces procurement costs by exploiting the supply curve information of prominent sellers to inform bidding decisions. The third strategy employs Markov Perfect Nash Equilibrium (MPNE) policies to model buyer behavior with known supply curves and introduces an algorithm to address scenarios where supply curve information is unavailable. The fourth strategy leverages Monte Carlo Tree Search (MCTS) to operate in the continuous action space of bid prices for optimized bidding in PDAs. In the tariff market, we develop strategies for generating attractive tariff contracts to build and retain a robust customer base. Using game theory, we demonstrate that maintaining an optimal market share significantly boosts net revenue. To achieve this, we propose both heuristic and learning-based approaches, with the latter employing multiarmed bandit (MAB) techniques for tariff generation. To address peak demand scenarios, we propose a demand response mechanism that incentivizes customers to shift their electricity usage away from peak hours. We present an optimal algorithm for allocating discounts that maximize expected peak demand reduction while adhering to budget constraints. Additionally, we introduce an MAB-based online algorithm to handle cases where customer responses to incentives are initially unknown. Finally, we integrate these strategies to develop our autonomous broker, VidyutVanika, for the PowerTAC simulation-based tournament. VidyutVanika competed against other brokers with the objective of maximizing profits. The results from the PowerTAC 2021 and 2022 tournaments demonstrate the effectiveness of our approach, with VidyutVanika emerging as the champion in both years.

Abstract

This thesis addresses the critical challenge of improving road safety by introducing novel approaches to predictive modeling of accident-prone zones and action recognition in critical traffic scenarios. It makes two key contributions: the early identification of accident-prone zones using Advance Driving Assistance System (ADAS) data and the development of IDD-CRS, a comprehensive dataset for action recognition in unstructured road environments. In the first study, geo-tagged collision alert data from a fleet of 200 ADAS-equipped city buses in Nagpur, India, is leveraged to proactively identify high-risk zones across urban road networks. Using Kernel Density Estimation (KDE), this study captures the spatiotemporal distribution of collision alerts, enabling the detection of emerging blackspots before accidents occur. A novel recall-based metric evaluates the alignment of these predicted zones with historical blackspots, while Earth Mover Distance (EMD)-based analysis identifies previously unreported accident-prone areas. This predictive framework provides civic authorities with actionable insights for targeted interventions, such as traffic-calming measures and infrastructure improvements, thereby enhancing public safety. The second part of the thesis introduces the IDD-CRS dataset, a large-scale collection of traffic scenarios recorded using ADAS and dash cameras. IDD-CRS fills a critical gap in existing datasets by focusing on complex interactions between vehicles and pedestrians, with scenarios such as high-speed lane changes, unsafe vehicle approaches, and near-miss incidents. With precise temporal annotations powered by ADAS technology, the dataset ensures accurate event boundaries, providing a robust benchmark for action recognition and long-tail action recognition tasks. It includes 90 hours of footage spanning 5,400 one-minute videos and 135,000 frames, with hard negative examples to challenge existing models. Initial benchmarks highlight the limitations of current video backbones in recognizing rare events, emphasizing the need for further advancements. Together, these contributions provide a holistic framework for improving road safety through proactive accident prevention and robust action recognition in traffic scenarios. By addressing both spatial accident prediction and temporal event recognition, this work offers foundational resources and actionable insights to advance research and practical solutions for safer road environments.

Abstract

Current pedagogical systems in dance majorly focus on performance training which has a major leaning towards entertainment service. As a result, the repertoire of movements has been restricted in catering to what is popular. This has led to a repetitive template-based training fitting into the audio-visual norms of the aesthetics of the time, which restricts the scope for further evolution of the dance form and curtails the possibilities of newer dance forms to emerge. Historically dance as a knowledge system was fundamentally taught in a form-neutral, styleneutral fashion with the ulterior motive of serving broad societal functions including ritualistic, devotional, and entertainment purposes. It is important to observe that only with the universal understanding of dance, the masters of yesteryears were able to design and formulate newer artforms in order to meet newer requirements of the clients and times. Any knowledge system has its universal fundamentals through which any of its regional practices can be taught. For example, music is generally taught through musical notes, considered to be universal and allows any musical system to be taught and practised. However, dance does not have common fundamentals through which all forms of dances can be taught. This research aims at deriving these universal fundamentals of dance systematically and devise a pedagogical system to disseminate these universals. To this end, the objectives are as follows. First, to establish the possibility of having universal dance fundamentals by reviewing the pertinent Dance Texts and understanding Dance Practices of Traditional Practitioners. Second, to systematically arrive upon categories of universal dance fundamentals to create form-neutral, style-neutral holistic Indian Dance Pedagogy catering to children and beginners. Third, to create a Pedagogical method to teach the universal dance fundamentals. Fourth, to develop a pedagogical tool as an aid to teach the universal dance fundamentals. Since the lower half body training serves as the fundamental training offered in any dance form, this research is focussed in creating a pedagogical system to understand the dance movements of the lower half of the body. In order to identify the universal fundamentals of dance, a survey of the lower-half bodypostures and movements mentioned in the Ancient Indian Dance texts such as Nāṭyaśāstra, Saṅgītaratnākara and Abhinaya Darpaṇa, and specific postures where the foot/feet are in contact with the floor were collected. As the major dance texts are not form- or style-specific, the postures mentioned in the dance texts are form-neutral and style-neutral. Based on analysis of these texts and from practice, the logical progression from one posture to another is identified. This gives rise to the logical prediction of the unmentioned postures in between the already mentioned postures of the texts. This enables a learner not to memorise the postures but gives a pedagogical method to understand movements as a progression based on logical parameters. Similarly the movements from the dance texts and practices were collected and logically arranged. In the process, it is observed that movements have three major components, that is, Posture, Transition and Rhythm in Transition. Posture is the static body position which serves as a marker to the movements. This way of understanding Postures springs out of an anatomical approach and the possible affordances of the joints, to make a posture. The postures are systematically arrived at based on eight parameters namely, part of the body in contact with the earth, Knee Positions, Knee angle, Feet angle, Body weight orientation, Foot position, Part of the foot touching the point and Feet Distance. Transition is dynamic. It is understood as a transitory movement from one posture to another. Despite similar postures across sports, martial arts, yogasana, dance and theatre could be common, the Transition, or the movement in between the postures is the factor which decides whether the act is of sports, martial arts, yogasana, dance or theatre. It is in fact the Transition which decides the form or style of dance. The Transitions are systematically arrived at based on seven parameters namely, lifted leg, point of lifting the leg, foot variation which is touching the point of lifting, part of the foot which is touching the point, trajectory traced by the foot as it reaches to the point, trajectory traced by the foot as it comes out of the point of lifting and foot activity. Furthermore, the parameter Rhythm in Transition determines the specific dance form and dance style. By understanding Postures, Transitions and Rhythm in Transition one can systematically understand the fundamentals of the movements, and more specifically dance movements. The mentioned three parameters served as a basis for a pedagogical framework, based on which a pedagogical meth

Abstract

This research work presents the design and FPGA implementation of a comprehensive radar signal processing system, focusing on de-interleaving, tracking, and pulse parameter estimation of multiple radar signals. The system encompasses end-to-end signal processing, starting from the down-converted intermediate radar signal as input, which is then processed to generate pulse descriptive words (PDW) containing crucial information such as frequency, bandwidth, pulse width, 3 Dimensional Direction of arrival (DOA), and pulse repetition interval (PRI), including PRI encoding technique. The system is capable of de-interleaving the signals of distinct types such as Pulse radar signals, frequency modulated continuous waves. The pulsed radar signals can be encoded in any type of PRI scheme such as fixed, jitter, and staggered PRI encoding techniques. Notably, the system is designed to track up to six non-coherent radar sources concurrently, providing situational awareness and signal detection capabilities. The accuracy achieved in the direction of arrival estimation is remarkable, with an error margin of less than 1°, for the signals with a signal-to-noise ratio (SNR) greater than 20dB. The experimental results obtained from the FPGA implementation demonstrate the effectiveness of the proposed system in comparison to other DOA estimation algorithms implemented on Field Programmable Gate Array (FPGA). The FPGA-based implementation of the De-interleaving of radar signals is tested in an anechoic chamber. The entire radar processing system is implemented using Verilog Hardware descriptive language. The test signal is obtained from the radar signal generator and the verification, validation is performed on Xilinx Virtex 7 FPGA platform. Simulations, verification, and synthesis are performed on Vivado tools. Overall the proposed system provides an efficient and reliable solution for direction finding, pulse parameter estimation and tracking multiple signals simultaneously which is crucial in various applications. The achieved accuracy in the direction of arrival estimation and the promising results obtained from the FPGA implementation showcase the potential to use it in radar-based applications such as defense, surveillance, and Electronic intelligence (ELINT) systems.

Abstract

Absolute separable (AS) quantum states are those states from which it is impossible to create entanglement, even under global unitary operations. It is known from the resource theory of non-absolute separability that the set of absolute separable states forms a convex and compact set, and global unitaries are free operations. We show that the action of a quantum switch controlled by an ancilla qubit over the global unitaries can break this robustness of AS states and produce ordinary separable states. First, we consider bipartite qubit systems and find the effect of quantum switch starting from the states sitting on the boundary of the set of absolute separable states. As particular examples, we illustrate what happens to modified Werner states and Bell diagonal (BD) states. For the Bell diagonal states, we provide the structure for the set of AS BD states and show how the structure changes under the influence of a switch. Further, we consider numerical generalization of the global unitary operations and show that it is always possible to take AS states out of the convex set under switching operations. We also generalized our results in higher dimensions. Several recent studies have demonstrated the utility of the quantum SWITCH as an important resource for enhancing the performance of various information processing tasks. In a quantum SWITCH, the advantages appear significantly due to the coherent superposition of alternative configurations of the quantum components which are controlled by an additional control system. Here we explore the impact of increasing the Hilbert-space dimension of the control system on the performance of the quantum SWITCH. In particular, we focus on a quantifier of the quantum SWITCH through the emergence of non-Markovianitxy and explicitly study their behavior when we increase the Hilbert-space dimension of the control system. We observe that increasing the Hilbert-space dimension of the control system leads to the corresponding enhancement of the non-Markovian memory induced by it. Our study demonstrates how the dimension of the control system can be harnessed to improve the quantum SWITCH-based information processing or communication tasks

Abstract

Remote labs are a technological breakthrough for accessing scientific knowledge, pushing the boundaries of traditional approaches to practical learning. They seek to provide students with unrestricted access to laboratory education and experimentation. Remote labs further prove invaluable in today’s times when pandemics such as COVID-19, geopolitical crises, and climatic conditions constantly threaten students’ access to practical learning in their institutions. Remote labs in such circumstances allow students remote access to practical equipment and laboratory resources. However, most of the developed remote labs today, either run on private networks, are specific to certain science domains, are too costly to replicate or require software installations, all of which hinder universal access to knowledge and education. With this motivation, the work presented in this thesis focuses on building a scalable software framework for real-time remote experimentation for Internet of Things (IoT)-based remote labs with lowlatency video streaming. This thesis is structured to comprise two parts. First, it explores the components and sequence flow of the developed end-to-end software architecture, emphasising the key design decisions for implementing the software solution and video streaming. It also discusses the introduction of an interoperability layer to support multiple IoT platforms and the implementation of a peer-to-peer (P2P) video streaming. The work further evaluates the developed architecture for its latency and capability of handling larger loads as the system scales. Secondly, the thesis also proposes the approach of multi-user multiplexing to address the scalability of remote labs hosting science experiments with near-instantaneous outputs based on electric currents, such as Kirchhoff’s laws. Such experiments and hardware nodes present a unique case compared to other practical experiments involving mechanical actuation. Multi-user multiplexing aims to provide users with concurrent access to the hardware nodes without impacting other users’ experience. The architecture proposed earlier can be modified slightly and adapted using WebSockets to facilitate the implementation of multi-user multiplexing. The modified WebSocket-based architecture constitutes three layers in contrast to the proposed four-layer architecture, and the two have been compared for their performance on latency and round trip time as part of this work

Abstract

A document has information emerging out of the existence of various physical entities or regions such as headings, paragraphs, figures, captions, tables, and backgrounds along with the textual content. To decipher the information in a document, a human reader uses a variety of additional cues, such as context, conventions, and information about language, script, location, and a complex reasoning process. Millions of documents are created and distributed daily over the Internet and printed media. Understanding, analyzing, sorting, and comparing a massive collection of documents in a limited time is a hectic job for humans. Here, the automatic document image understanding systems (DIUS) help humans do this tedious task within a limited time. The DIUS have typically a document image layout segmentation module and information extraction modules. This thesis focuses on the challenging problems related to document image layout segmentation in various types of documents and their applications. In this thesis, first, we analyse the various document images using deep features. The deep features are the features extracted using a pretrained deep neural network. To study deep texture features, we propose a deep network architecture that independently learns texture patterns, discriminative patches, and shapes to solve various document image analysis tasks. The considered tasks are document image classification, genre identification from book covers, scientific document figure classification, and script identification. The presented network learns global, texture, and discriminative features and combines them judicially based on the nature of the problems. We compare the performance of the proposed approach with state-of-the-art techniques on multiple publicly available datasets such as Book covers, RVL-CDIP, CVSI and DocFigure. Experiments show that our approach obtains significantly better performance over state-of-the-art for all tasks. Next, we focus on the problem of document image layout segmentation and propose a solution for a class of document images, including historical, scientific, and classroom slide document images. The historical document image segmentation problem is modeled as a pixel labeling task where each pixel in the document image is classified into one of the predefined labels, such as text, comment, decoration, and background. The method first extracts deep features from the superpixels of the document image. Then, we learn SVM classifier using these features and segment the document image. The pyramid pooling module is used to extract the logical regions of scientific document images. In the classroom slide images, the logical regions are distributed based on the location of the image. To utilize the location of the logical regions for slide image segmentation, we propose the architecture, Classroom Slide Segmentation Network (CSSN). The unique attributes of this architecture differ from most other semantic segmentation networks. We validate the performance of our segmentation architecture using publicly available benchmark datasets. Next, we analyze the output regions of document layout segmentation. Figures used in the documents are the complex regions to decipher the information by a DIUS. Hence, document figure classification (DFC) is an important stage of the DIUS. The design of a DFC system required well-defined figure categories and datasets. Existing datasets related to the classification of figures in the document images are limited in size and category. We introduce a scientific figure classification dataset named DocFigure. The dataset consists of 33K annotated figures of 28 different categories present in the document images, which correspond to scientific articles published in last several years. Manual annotation of such a large number (33K) of figures is time-consuming and cost-ineffective. We design a web-based annotation tool that can efficiently assign category labels to many figures with the minimum effort of human annotators. To benchmark our generated dataset on the classification task, we propose three baseline classification techniques using the deep feature, deep texture feature, and both. Our analysis found that the combination of both deep and texture features is more effective for document figure classification tasks than individual features. Finally, we propose the application backed by the research of this thesis. Slide presentations are an effective and efficient tool for classroom communication used by the teaching community. However, this teaching model can be challenging for blind and visually impaired (VI) students as such students require personal human assistance to understand the presented slide. This shortcoming motivates us to design a Classroom Slide Narration System (CSNS) that generates audio descriptions corresponding to the slide content. This problem poses an image-to-markup language generation task. Ext