Abstract

The social isolation required by the pandemic further amplified the internet’s utility as a medium of communication and information for people with digital access. Social media was one of the most popular forms of communication among young people with digital access during the COVID-19 pandemic. Consequently, crucial debates and discussions about the pandemic crisis have also developed on social media platforms, making them a great primary source to study the experiences of specific groups and communities during the pandemic. Furthermore, online news media outlets were an important source of information for people with digital access during the COVID–19 pandemic. This study consisted of research on social media and online news websites in order to understand the experiences of as well as attitudes towards LGBTQ+ people during the pandemic. We used LDA topic modelling and sentiment analysis on Reddit data to understand the themes and attitudes in circulation within five subreddits devoted to LGBTQ+ experiences and issues. In the process, we attempt to make sense of the role that social media platforms may have played in the lives of LGBTQ+ people who were online during the pandemic (10 March 2020–31 August 2021) and whether this was marked by any continuities or discontinuities from before the pandemic period (January 2019–December 2019). We also examined articles from online editions of two English–language Indian newspaper websites, which differed vastly in their circulation figures—The Times of India and The Indian Express. Focusing on the period spanning March 2020 to August 2021, we analysed articles from our chosen newspaper websites using distil–RoBERTa–base and ChatGPT–3.5 for sentiment analysis and BERTopic for topic modelling. Furthermore, we manually analysed a part of our dataset obtained from Reddit and online newspaper websites to arrive at a nuanced, granular understanding of our quantitative results. The results of our research on Reddit reveal that institutions such as the health care system, justice system, and legislative system perpetuated systemic inequalities against LGBTQ+ communities, thereby adding to the pre–existing stigma against them during a global crisis. Furthermore, the results from our topic model suggest a shift in the discussions of prominent topics in the subreddits from before the pandemic period, with a preponderance of discussions on political events. Similarly, the results from our sentiment analysis show an increase in positive comments and neutral comments and a decrease in negative comments, which does not necessarily indicate a loss of users’ interest in the subreddits. While analysing online newspaper websites, we found a contrast between the quality, quantity, and attitudes prevalent across The Times of India and The Indian Express during the COVID–19 pandemic in the context of LGBTQ+ communities. Our topic modelling results indicate more meaningful coverage by The Indian Express compared to The Times of India, which, however, does not mean sufficient coverage by The Indian Express to provide a comprehensive picture. Similarly, our sentiment analysis results indicate a difference in prevailing sentiment as depicted by the two models. Both newspaper websites exhibited a greater focus on transgender communities. Our research on Reddit has demonstrated what non–cis–heteronormative spaces—virtual and real— can look or feel like. By offering a more rigorous engagement with the data available on social media platforms, this research contributes to current debates in queer and social media studies about the empowering or disempowering nature of social media platforms for LGBTQ+ voices online. Furthermore, it captures the visibility and representation of the LGBTQ+ communities during the pandemic in online Indian news media outlets. By doing so, we contribute to the larger understanding of how Indian media narrated the lived realities of LGBTQ+ communities and what attitudes such representation reveal towards vulnerable communities in India during the pandemic. Overall, this thesis expands the understanding of the kind of discursive spaces and representation offered by online platforms for LGBTQ+ communities during a global crisis, demonstrating both possibilities and potential as well as challenges and problems.

Abstract

In autonomous driving, we have multiple computer vision tasks like object detection, semantic seg- mentation, and instance segmentation which plays a crucial role in perceiving the environment around the vehicle. Understanding the behaviour and performance of such tasks helps improve and address the key issues that are inherent in the system. There can be issues which are latent in the deep learning architecture and also in the datasets on which the deep learning models are trained and tested. In this thesis, we benchmark the performance of various popular deep learning models on road scene datasets for various computer vision tasks and also formulate open-world object detection on road scenes by addressing the inherent issues present in road scene datasets. In the first part of the work, we aim to understand the performance and behaviour of various deep learning models on road scene datasets; Cityscapes, IDD, and BDD. Object detection, semantic seg- mentation, and instance segmentation form the bases for many computer vision tasks in autonomous driving. The complexity of these tasks increases as we shift from object detection to instance seg- mentation. The state-of-the-art models are evaluated on standard datasets such as PASCAL-VOC and MS-COCO, which does not consider the dynamics of road scenes. Driving datasets such as Cityscapes and Berkeley Deep Drive(BDD) are captured in a structured environment with better road markings and fewer variations in the appearance of objects and background. However, the same does not hold for Indian roads. The Indian Driving Dataset(IDD) dataset is captured in unstructured driving scenarios and is highly challenging for a model due to its diversity. This work presents a comprehensive evaluation of state-of-the-art models on object detection, semantic segmentation, and instance segmentation on road scene datasets. We present our analyses and compare their quantitative and qualitative performance on structured driving datasets(Cityscapes and BDD) and the unstructured driving dataset(IDD); under- standing the behavior on these datasets helps in addressing various practical issues and helps in creating real-life applications. In the second part of the work, we model open-world object detection on road scenes since object detection is a crucial component in autonomous navigation systems. Current object detectors are trained and tested on a fixed number of known classes. However, in real-world or open-world settings, the test set may consist of objects of unknown classes; this results in the unknown objects being falsely de- tected as known objects leading to the failure in the decision making of autonomous navigation systems. We propose Open World Object Detection on Road Scenes (ORDER) to resolve the aforementioned problem. We introduce Feature-Mix that widens the gap between known and unknown classes in latent feature space and improves the unknown object detection in the ORDER framework. We identify the inherent problems present in autonomous datasets: i) a significant proportion of the dataset comprises small objects and ii) intra-class bounding box scale variations. We address the problem of small object detection and intra-class bounding box variations by proposing a novel focal regression loss. Further, the detection of small objects is improved by curriculum learning. We present an extensive evaluation of two road scene datasets: BDD and IDD. Our experimental evaluations on BDD and IDD shows con- sistent improvement over the current state-of-the-art method. We believe that this work will lay the foundation for real-world object detection for road scenes.

Abstract

In spite of the vast work in the area of Maximal Frequent Subgraph (MFS) mining, current algorithms do not scale to large databases with numerous graphs. Even the smallest search space required by current MFS algorithms for large databases is too huge to work within the main memory alone, making the algorithms I/O bound. Added to this is the expensive cost of subgraph isomorphism operation, which is commonly used for frequency computation. This thesis explores partition-based secondary memory algorithms and proposes Partition-based Frequent Subgraph Mining using Maximum Common Subgraph (PMCS), that can make maximal frequent subgraph mining for large databases viable. The naive application of the partition-based approach is inefficient due to the need to recompute subgraph frequency and the possibility of storing intermediate subgraphs on the disk. In PMCS, we overcome these drawbacks by intelligently avoiding redundant computations and performing frequency computation operations optimally using the Maximum Common Subgraph operation. Our intermediate results are also small enough to fit in the main memory. While existing algorithms fail to scale beyond 300k, our results demonstrate that PMCS scales to databases of up to 1000k graphs with an average of 25-30 edges per graph.

Abstract

Spoken utterances have an inherent structure in the sense that some words seem to group naturally together and some words seem to have a notable break or disjuncture between them. This can be described in terms of prosodic phrasing, meaning that a spoken utterance has a prosodic phrase structure, similar to how a written utterance has a syntactic phrase structure. Phrase breaks in natural speech are important; they are physiologically essential, help emphasize content, and improve the intelligibility of speech. The process of inserting phrase breaks in an utterance is called phrasing. In the context of speech synthesis, phrasing is a crucial step. It breaks long utterances into meaningful units of information and improves the intelligibilty of the synthesized speech. Traditional methods of phrase break prediction have used discrete linguistic resources like part-ofspeech (POS) sequence information for modeling these breaks. These methods cannot be used for languages where the necessary linguistic resources are not readily available, resulting in unsupervised methods of inducing word representations which can be used as surrogates for POS tags in phrase break prediction. However, these methods are not suitable in the context of Indian languages, which are aggutinative in nature resulting in an increase in vocabulary size. This thesis presents the use of word terminal syllables (last syllable of the word) to model phrase breaks. We demonstrate the correlation between these terminal syllables and acoustic breaks found in the speech signal. We utilize these terminal syllables in building models for phrase break prediction from text in six Indian languages and demonstrate by means of objective and subjective measures that these models perform as well as traditional models using POS sequence information. The discrete linguistic representations (like POS tags, induced POS tags, word-terminal syllables) used by traditional methods of phrase break prediction require a hard classification of words into a set of predefined discrete classes, which raises issues when there is ambiguity in the linguistic representation of a word. Moreover, such a representation does not capture the co-occurrence statistics of the words, i. e., they do not take into account the distributional behaviour of words. Both these issues can be addressed by the use of continuous dimensional representations of words also known as word embeddings. This thesis presents a neural network architecture to induce task specific word embeddings for phrase break prediction. We show that our proposed architecture combines feature induction and phrase break prediction in a single framework and thus avoids the two stage process required while using features derived from Latent Semantic Analysis (LSA). We train our model on audiobook data and show that these task specific word features are better word representations as compared to those derived using LSA, for phrase break prediction. With the advent of deep learning, there have been attempts to apply deep neural networks (DNNs) to phrase break prediction. While deep neural networks are able to effectively capture dependencies across features, they lack the ability to capture long-term relations that are spread over time. On the other hand, recurrent neural networks (RNNs) are able to capture long-term temporal relations and thus are better suited for tasks where sequences have to be modeled. This thesis presents the use of RNNs for phrase break prediction, and shows by means of experimental results that they perform better than DNNs. Traditional techniques for learning word embddings learn static or context-free word embeddings, meaning that for each word in the vocabulary, one fixed embedding is learnt for all possible contexts in which that particular word appears in the text corpus. While static embeddings have proved to be effective in NLP applications, they do not take into account the fact that words have different meanings in different contexts. To address this issue, context-sensitive word representations, also called dynamic or contextual word embeddings were developed. The most popular technique for learning dynamic word embeddings is BERT (Bidirectional Encoder Representations from Transformers). This thesis presents work on phrase break prediction using bidirectional encoder representations learnt from fine-tuning a pretrained BERT model, with an additional token classification layer, on phrase break prediction. We show that representations using this model outperform task-specific static word embeddings learnt using a BLSTM token classification model trained from scratch. Finally, this thesis presents work which attempts to answer the following questions: (a) Is there any utility in incorporating an explicit external phrasing model in an End-to-End TTS system? and (b) How do you evaluate the effectiveness of a phrasing model in an End-to-End TTS system?

Abstract

Ancient manuscripts were among the first forms of written communication, offering key insights into our past, covering literature, medicine, culture, philosophy, religion, and more. It is imperative to save these writings to identify and extract their hidden knowledge. Document collections frequently exhibit overlapping components, irregular patterns, dense layouts, extremely high aspect ratios, physical and chemical degradation (evident in ink-based manuscripts), text misalignment, and more. Compounding these difficulties are issues that may arise during the digitization processes, such as improper document positioning, inadequate illumination, and scanner effects. In this academic thesis, our main emphasis is on identifying and segmenting text lines inside these documents for downstream OCR applications with utmost precision. We devise a two-stage approach - SeamFormer- to identify special text baselines in palm-leaf manuscripts using a multi-task strategy via Vision Transformers (ViTs). In the first stage, we detect text strikethroughs, namely ’scribbles,’ which act as pointers to the location of text line segments within the document. The encoder-decoder architecture is used to analyze input image patches and produce two separate maps: a scribble map and a binary map. In the second stage, we post-process the prior stage outputs and generate a diacritic-aware global energy map. To generate the final precise text line polygons, we use a modified seam generation algorithm along with customized global maps. The prior methodology is further enhanced by the proposed LineTR model, a multi-task DETR (Detection Transformer) model that reconceptualizes the scribble generation as a geometric problem. This method simply generates line parameters for each text line present in the input image patch. This design decision enables the model to exhibit zero-shot behavior across diverse historical manuscripts. The state-of-the-art approach has been proven to generate precise text-line segmentation with a single ’unified’ model with minimal post-processing efforts, making it a strong candidate for image-to-OCR integration pipelines

Abstract

A Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is one of the primary barriers between notorious bots and legitimate human users. However, advancements in Artificial Intelligence (AI) have enabled malicious bots to circumvent CAPTCHA challenges effectively. As a result, several types of CAPTCHA have been rendered ineffective. In this work, we introduce Swiss Cheese CAPTCHA, a novel sensor-based solution designed to be easily solvable by humans while presenting multiple obstructions for bots (similar to the Swiss Cheese Model) even when the sensor outputs can be predicted and interfered with. We leverage a range of human cognitive abilities and Generic Sensor API in modern devices to provide robust protection against automated attacks by making it more computationally expensive for bots to produce a valid answer within a stipulated time. We conducted two user studies to assess our proposal’s effectiveness: one involving 116 participants to assess the likability and improvise the design, and the other, with 107 participants, to investigate the impact of improvised design changes on cognitive abilities. Our results from these studies show an average completion time of 4.76 seconds and 6.12 seconds, with a success rate of 90.3% and 83.25%, respectively. By analyzing the 2141 resultant trajectories from both user studies, we assess the learnability, error recovery rate, efficiency, and satisfaction of users using the scheme. Finally, we devise an automated attack against our proposal to analyze its security in the real world; we find the probability of attack success is low. We also make our dataset available for further research.

Abstract

This thesis presents a comprehensive exploration of Multi-Object Multi-Part Scene Parsing in 2D images, showcasing significant advancements through two novel approaches, FLOAT and OLAF, each tailored to enhance scene parsing performance and scalability. The first paper introduces FLOAT, a factorized label space framework designed to independently predict object categories and part attributes, thereby simplifying the segmentation task and enhancing scalability. Notably, FLOAT incorporates a unique ’zoom’ refinement technique at inference time, significantly elevating segmentation accuracy, particularly for smaller objects and parts. Empirical results on the Pascal-Part datasets underscore FLOAT’s superior performance, achieving notable improvements in mean Intersection Over Union (mIOU) and segmentation quality IOU (sqIOU), especially on the most comprehensive PascalPart-201 dataset, reflecting its effectiveness in handling diverse and complex scenes. The second paper delves into OLAF, a plug-and-play methodology that augments traditional RGB inputs with object-based structural cues to better capture the complexities of scene structures. This approach leverages a weight adaptation technique, allowing pre-trained RGB models to seamlessly integrate augmented data, thus stabilizing the optimization process. Additionally, the introduction of the LDF encoder module aids in providing low-level dense feature guidance, enhancing the segmentation of smaller parts. OLAF demonstrates its versatility across various architectures and datasets, achieving significant mIOU gains on multiple Pascal-Part benchmarks, highlighting its broad applicability and robust performance enhancements in challenging segmentation scenarios.

Abstract

In Indian urban and rural driving scenarios, small objects are pervasive and often crucial for safe navigation. These objects can include pedestrians crossing roads, children playing near streets, cyclists, stray animals, as well as small vehicles like scooters and motorbikes. Additionally, traffic signs, signal lights, potholes, and road markings (such as lane dividers or zebra crossings) are often small in size but essential for driving decisions. In such contexts, missing or inaccurately segmenting these small objects can lead to critical errors in detection, causing accidents or delays in the vehicle’s decision-making process. Automated understanding of such objects need detection and segmentation to start with. Semantic Segmentation is a critical task in computer vision with a wide range of applications. The objective is to partition an image—a collection of pixels—into distinct labeled regions, each corresponding to specific objects or parts of the scene. This process is crucial for scene understanding and enables the localization of objects within the image. Over time, significant progress has been made in semantic segmentation, especially with the advent of deep learning. The advances in this area have revolutionized computer vision, pushing beyond traditional methods and achieving remarkable improvements in performance. When discussing semantic segmentation, we often focus on datasets, the objects within those datasets, and their corresponding segmentations. While many datasets exist for road scenarios, particularly those representing Western road conditions, there is relatively little research on road conditions specific to India. One notable exception is the Indian Driving Dataset (IDD), a dataset specifically designed for semantic segmentation of Indian road scenarios. Road and driving datasets typically contain objects of varying sizes within each class label. These objects can be broadly categorized into three types: small, medium, and large. The importance of segmentation is well understood across several domains such as medical imaging, autonomous vehicles, aerial imagery, robotics, surveillance, and industrial automation. However, one of the most challenging problems in segmentation is the segmentation of small objects. Small object segmentation is particularly difficult due to factors such as (i) the limited number of pixels representing small objects, (ii) class imbalance during training, and (iii) the inherent challenges posed by small object representations. These factors hinder the performance of deep learning architectures, making it harder for modern techniques to accurately handle small objects. This issue becomes evident when evaluating deep learning models, as performance metrics tend to be significantly worse for small objects. In this study, we delve into the challenges of segmenting small objects and explore potential strategies for improving algorithm design for this task. It is widely acknowledged that loss functions play a key role in segmentation performance, alongside the architecture of deep learning models. Indeed, the segmentation of small objects is highly influenced by the choice of loss function during model training. Various small objects commonly encountered in road scenarios are highlighted in this study, emphasizing their importance in Indian road environments. Objects such as pedestrians, traffic signs, and traffic lights—despite their small size—are crucial for tasks like autonomous navigation and driver assistance systems. The Indian Driving Dataset (IDD) offers a valuable resource, containing a wide range of small objects across different class labels, some of which dominate specific categories. These objects are captured and annotated in real-world, unstructured environments, making this dataset particularly relevant for segmentation tasks in Indian road scenarios. Therefore, our focus in this study is primarily on detection and segmentation of small objects within the IDD dataset and explore the impact of loss functions.

Abstract

Principal component analysis(PCA) is one of the most popular linear dimensionality reduction techniques in machine learning. In this thesis, we present a method for performing PCA on encrypted data using a homomorphic encryption scheme. We used the CKKS homomorphic encryption scheme for our purpose since it is most suitable for machine learning tasks allowing approximate computations on real numbers. In this thesis, we propose a new Homomorphic PCA(HPCA) algorithm that performs PCA. Unlike previously proposed algorithms, our HPCA algorithm is non-interactive in nature. This requires an approximation of the inverse sqrt function. This thesis presents two methods to approximate and securely perform the inverse sqrt function using CKKS homomorphic encryption scheme - Constrained Linear Regression and Pivot-Tangent method. Since the CKKS homomorphic scheme allows only the computation of polynomial functions, we propose a method to approximate the inverse sqrt function polynomially. Ultimately, we implement our approach for the inverse sqrt function. We provide an implementation of our method for the inverse sqrt function. We use the inverse sqrt approximations in our HPCA algorithm to make it non-interactive. In the end, we present the experimental results of our proposed HPCA algorithm on a few datasets computing a few principal components. We measure the R2 score on the reconstructed data and use it as an evaluation metric for our HPCA algorithm.

Abstract

This thesis explores the dual objectives of reviewing material characteristics of soft polymers for microfluidic and Lab-on-a-Chip (LOC) devices and developing a cost-effective, flexible microheater for LOC applications. Soft materials such as polydimethylsiloxane (PDMS), hydrogels, thermoplas- tic elastomers, and conducting polymers are analyzed for their mechanical, thermal, chemical, optical, and biocompatibility properties. These polymers play a crucial role in LOC systems due to their abil- ity to provide flexibility, durability, and compatibility with biological samples. The review delves into their fabrication techniques, solubility in common solvents, adhesion, and surface modification meth- ods, highlighting their relevance for applications such as diagnostics, cell culture, and chemical assays. Experimental studies further assess their optical properties and cytotoxicity, offering a comprehensive understanding of their potential for use in biomedical and chemical systems. By comparing the advan- tages and limitations of these materials, this work aids in optimizing material selection for enhanced performance and sustainability in LOC applications. The developed flexible microheater utilizes nichrome wire embedded in a PDMS substrate, chosen for its affordability and durability. The microheater operates within a temperature range of 40°C to 150°C, with precise control achieved through a pulse-width modulation (PWM)-based circuit and feed- back from a 100k Ω NTC thermistor. This setup provides closed-loop temperature regulation, ensuring stability and adaptability for diverse applications. The microheater’s efficacy is demonstrated in two sig- nificant applications: glucose detection and the synthesis of silver nanoparticles (AgNPs). In the glucose detection application, the microheater integrated into a microwell device reliably facilitates Benedict’s test, confirming its potential for point-of-care diagnostics. The microheater is also employed in the syn- thesis of AgNPs using green chemistry principles, utilizing ascorbic acid as a reducing agent and sodium citrate as a stabilizer. This process yields spherical nanoparticles with UV-Vis absorption peaks charac- teristic of AgNPs (between 405 nm and 411 nm). Both applications underline the microheater’s capacity to deliver consistent performance while maintaining low fabrication costs. Experimental results validate the heater’s capability to reach steady-state temperatures quickly and efficiently. The integration of the PWM-based control system ensures rapid response times and enhanced accuracy, addressing challenges in conventional heating systems. Furthermore, the cost of the microheater device is kept below INR 1000, making it an accessible solution for resource-constrained settings.