Abstract

Air pollution monitoring is crucial for assessing the health risks posed by pollutants, identifying pollution sources, and developing effective strategies for reducing pollution and protecting public health. The Indian government has taken initiatives, which provides real-time air quality data to the public and raises awareness of air pollution levels. The initiative measures several pollutants, including Particulate Matter (PM), and categorizes Air Quality Index (AQI) level into six categories ranging from “Good” to “Severe”. The air pollution sensors employed for calculating the AQI are associated with several limitations. Consequently, the central objective of this thesis is to estimate the AQI without relying on any pollution sensor. To achieve this goal, the proposed methodology employs real-time traffic data and images to estimate the AQI in real-time. Firstly, this thesis propose an image processing based technique to estimate the AQI levels using traffic images and weather parameters, which can be used in rural and sub-urban areas where sensors are hard to deploy and maintain. This approach allows for real-time estimation of AQI through smartphones and can be used portably. The proposed method achieves up to 90% accuracy for the AQI classification. Furthermore, a feature-rich dataset is made publicly available to encourage further research. After that, a novel method based on the Internet-of-Things (IoT) and Machine Learning (ML) is proposed to estimate the AQI using real-time traffic data. To build a rich traffic dataset, PM monitoring nodes were deployed in 15 diverse traffic scenarios across Indian roads, and digital map service providers were utilized. Three ML models, namely Random Forest (RF), Support Vector machine (SVM), and Multi Layer Perceptron (MLP), were trained on this dataset to predict AQI categories into five levels. Experimental results demonstrate an accuracy of 82.60% and an F1-score of 83.67% on the complete dataset. In addition, individual node datasets were used to train ML models, and the behavior of AQI levels was observed.

Abstract

Sign languages have possibly been around for longer than spoken languages. What is interesting about them is that they evolve similarly to spoken languages and tend to have complex grammar and vocabulary. They are mostly used in day-to-day communications by the hearing-impaired community or by people who interact with them on a daily basis. It is even considered by some to be their mother tongue. Indian Sign Language is used mainly in the Indian subcontinent, mostly by the hearing-impaired community. Apart from investigations that have a more or less sociological approach, research results about the linguistic structure of sign language in India had been very scarce until the last decade. However, it has gained prominence in recent years. The 11th Five Year Plan (2007-2012) acknowledged that the needs of people with hearing disabilities had been relatively neglected and envisaged the development of a sign language research and training center, to promote and develop sign language and training of teachers and interpreters. As such, some work is being done in translating spoken languages in various forms into ISL. But most of the work done in this regard always focuses on the accuracy of the signed videos while grammar takes a back step. In 2 we show that these tools are mainly used by hearing-impaired students who are trying to pick up English as a second language. Our work hence focuses on getting as much grammatical accuracy as possible for converting English to Indian Sign Language. Here we tackle the problem of converting written text into ISL video by using Text for Indian Sign Language as an intermediary step. To do this, we use rule-based translation methods and apply techniques such as multi-word expression detection and synonym substitution in the source language. In this thesis, I shall try to explain the advantages of taking this route as well as provide some background into the world of sign languages with a focus on Indian Sign Language (ISL).

Abstract

Unsupervised text embedding models are ubiquitous in the field of Natural language Processing. These models embed words, sentences or documents as vectors in an Euclidean space with the principle that textual information that are semantically similar have similar representations i.e they lie close to each other in the semantic space. Word2vec and GLoVe are the most popular examples of such models. Both these models provide efficient training of word embeddings and provide evaluation methods like word similarity and word analogy tasks that proves the effectiveness of these models. However, they have their fair share of drawbacks. For instance, there is little to no explanation on why word vector summation solves word analogy. These models also suffer from meaning conflation deficiency where multiple senses of a word are represented by one single vector and thus lead to inaccurate semantic modelling. This is also an issue with document and sentence embeddings which span multiple words, phrases and topics. Existing unsupervised models which tackle meaning conflation deficiency perform sense representation where each senses of a word are represented by an individual vector. This is done by modifying the Word2vec skip-gram algorithm and adding some extra constraints on the vector embedding space. Instead of adding extra constraints on the Word2vec algorithm, we aim to address this issue of by exploiting the geometry of the embedding space. We propose three unsupervised text embedding models that embed texts in Riemannian manifolds by integrating the linguistic principles of Word2vec and GLoVe with optimization tools from differential geometry. The first and the second model uses the same joint modelling framework but embeds both words and documents in the Grassmannian manifold and a custom product manifold respectively. Both models generate quality document embeddings given by their evaluation results in document clustering and document classification tasks. The third model embed words in Spectahedron manifold where each word is a matrix whose eigenvectors correspond with the multiple senses of that word. Finally, we provide a Lie-group theoretic understanding of linear substructures present in Word2vec and GLoVe that solves word analogy. Lie groups are differentiable manifolds with a group structure. Some basic properties of these groups show that the linear substructures are present due to the tangent space of the Identity element of the group.

Abstract

Code-mixing (CM) is a common linguistic phenomenon, especially in multilingual communities, where speakers blend two or more languages or dialects in a single sentence or conversation. With English being the second language in India, code-mixing has become a predominant practice, particularly in urban areas. It is common for a Hindi or Telugu speaker to switch to English in the same sentence or utterance. The surge in code-mixing can be attributed to the rise of social media, which has provided a platform for people from diverse linguistic backgrounds to communicate. In such contexts, code-mixing has become an effective way to express oneself using words and phrases from multiple languages. Code-mixing has been of interest to sociolinguists for a long time. Studying its functional form from a sociolinguistic perspective has become a significant phenomenon along with its linguistic structures. In a multilingual country like India, politicians use language and linguistic mechanisms to establish relationships with people. Our study focuses on using code-mixing in Telugu political speeches to understand the factors responsible for their usage levels in various social settings and communicative contexts. As part of our analysis, we have compiled a set of rules that capture dialectal variations between the Standard and Telangana dialects of Telugu. Building NLP systems for code-mixing can help analyze sentiment and conversations in social media. It can also facilitate machine translation, helping people who converse in multiple languages to communicate more efficiently. The informal nature of code-mixed text presents a significant challenge for NLP systems. It results in a wide range of language variations that include non-standard abbreviations, contracted spellings, and informal grammatical structures. These challenges collectively result in a scarcity of quality code-mixed data that can be used for building NLP systems. We propose a hybrid methodology to generate code-mixed text. The proposed hybrid methodology combines rule-based and statistical approaches to convert a monolingual Telugu sentence into a codemixed sentence in English and Telugu dialects. Due to the complexity of the proposed hybrid approach, we propose a fine-tuned neural machine translation method to generate high-quality code-mixed sentences using minimal gold-standard corpus. We use filters from the gold corpus to ensure that the synthetic training data for the models is of high quality, resulting in improved performance of the neural machine translation models. Considering the recent success of pre-trained models such as mT5 and mBART, we fine-tuned these models. Moreover,our approach outperforms the current systems trained on synthetic data for code-mixed generation in Hindi-English. Apart from Hindi-English, the approach performs well when applied to Telugu, a lowresource language, to generate Telugu-English code-mixed sentences. It is crucial to investigate the effectiveness of filtering techniques for generating high-quality code-mixed data, especially in the case of low-resource languages. Moreover, exploring the application of one-shot and zero-shot learning techniques to determine whether the models are trained to generate code-mixed sentences in general or are specific to the languages they are trained on is also a promising future direction.

Abstract

The exchange of information between humans involves the use of speech and text and is known as Natural Language. Each day, people communicate with each other in various languages through speech or text, sharing a vast amount of information. The data produced through natural language communication can offer valuable insights despite being ambiguous, unstructured, and noisy. However, computers cannot interpret natural language on their own yet. To fully comprehend this data and respond intelligently, computers need the ability to understand and emulate human language. This is where Natural Language Processing (NLP) comes in - it is a branch of Artificial Intelligence that focuses on enabling machines to read, comprehend and derive meaning from human languages. NLP integrates the disciplines of linguistics and computer science. It decodes language, its structure, rules, and creates models capable of comprehending, analyzing, and extracting important information from both text and speech. An abundance of information is available in the form of text, including books, documents, articles, social media posts, and more. A document, one of the oldest forms of information exchange, refers to written, printed, or electronic material that is created to facilitate the exchange of information from its author to its intended audience. These files contain valuable information that can significantly benefit business activities. With the use of NLP applications, insights can be extracted from text data. Enterprises utilize NLP applications for various purposes, ranging from document understanding, information extraction, or providing answers to common questions. In this thesis, we develop techniques for deeper analysis and understanding of documents that are commonly used in enterprises. A contract is a frequently used type of document in the corporate world. Contracts are agreements between two or more parties, that govern what each party can or cannot do and are usually dense in information. Automatically extracting key components or components that contain rare or novel information from these large documents makes reviewing contracts easier. Nevertheless, it can be a challenging task as the key and novel components are not present in isolation within the contract. Extraction of significant components (key components + novel components) from contracts aims to simplify the end user’s comprehension and reduce dependency on legal experts for reviewing contracts. In this thesis, we introduce approaches for the automatic identification and extraction of significant components from a contract. We propose a Bidirectional Encoder Representations from Transformers (BERT) based model that automatically identifies or highlights significant components of a contract. In the corporate world, reports are also a frequently encountered type of document. A report is a document that provides information and analysis on a particular topic or issue. Reports are used to convey important information to stakeholders, such as managers, executives, investors, and customers. The vast data available in these reports have the potential to revolutionize datadriven analysis. Causality identification and span detection is one such data-driven task. The relationship between two entities where one causes another event to happen is known as cause and effect. We explored various transformer-based models that help in classifying sentences as well as identifying spans in a sentence.

Abstract

Hospital buildings are expected to remain Occupiable after earthquakes to cater to the post-disaster response and rescue efforts. This requires a stiffer and stronger structural system compared to the commonly adopted moment frames in normal buildings; wallframe systems are recommended in these buildings. But, in addition to merely providing the wall-frame structural system, it is also essential to adequately design the wall-frames to meet the desirable Occupiable seismic performance. Appropriate seismic structural configuration, Structural Plan Density (SPD) of structural walls, and seismic design parameters are presented of typical hospital building located in Seismic Zone IV, that helped meet the preferred performance. A displacement-based limit state of structural damage in line with classic displacement demand estimation is proposed, to confirm the performance. Fiber inelasticity in walls and moment frames in study frames and buildings are defined, for performing displacement controlled nonlinear static pushover analyses and nonlinear time history analyses in commercial software PERFORM3D. Alongside, limit states of structural damages, namely yielding of longitudinal reinforcement in beams or columns in tension, yielding of vertical longitudinal reinforcement in structural walls in tension, crushing of confined concrete in compression in beams, crushing of confined concrete in compression in columns, and spalling of unconfined concrete in compression in structural walls are also monitored to grade the damages, and in turn the seismic performance of wall-frames. Results suggest, numerical models with fiber inelasticity help predict nonlinear behaviour reasonably well. Moment frames do not provide preferred seismic performance, but wall-frames with plan-aspect ratio more than 4 enhances lateral stiffness, strength, and ductility of wall-frames, thereby meeting the preferred performance. The major findings of the study are at least 3% SPD of structural walls is essential in typical wall-frame hospital buildings to ensure Occupiability performance after earthquakes. Furthermore, Column-to-Beam Strength Ratio of minimum 2 is also required in hospital buildings to remain Occupiable.

Abstract

We, humans, are social beings, and our communication is the most evolved and wellstructured form of a communication system that we are aware of. An essential aspect of human communication that helps humans bond faster and develop a sense of closeness is the use of humor. Humor that occurs as part of a conversation is known as conversational humor. Conversational humor is a type of humor that is unique and contrary to what it may seem like. It is more than just plugging canned jokes into a conversation. It requires the use of certain techniques and the presence of at least two interlocutors who understand the context of the conversation. The first step towards understanding conversational humor is to identify the different types into which it can be categorized and the techniques that are used to generate each type of humor. Current studies on this front either consider only a subset of these types and techniques or are domain specific. To tackle these challenges, we first propose a hierarchical annotation schema which allows us to get a comprehensive overview of conversational humor. For this task, we use a famous Telugu play, Kanyasulkam, and consider humorous utterances from this play as the dataset. This schema includes tags for type, technique, and benignity and considers cultural nuances in the text, making it an extensive schema for conversational humor. Further, to test the universality of the schema, we built a dataset of a different domain (Covid-19-based humor) and language (English). This dataset was annotated using a part of the annotation schema containing the type and technique tags. Two more tags viz. “Situation” and “Relevance” were added in the schema to help make the dataset more valuable as a standalone dataset which can be used by researchers from other fields like marketing, sociology, etc. The effectiveness of this dataset is tested with the help of various experiments for binary as well as multi-label multi-class classification using state-of-the-art ML models including but not limited to BERT, RoBERTa, BerTweet, etc. Based on the accuracy and analysis from the experiments, we can show that the annotation schema is universal in terms of language and domain. Such a classification of data can be used to accelerate the annotation process for humor data, and this annotated data can be used for various purposes like marketing, connecting with a target audience based on the relevance tag, and aiding research in the field of conversational humor for building humorous chatbots, and more human-like interactive systems.

Abstract

This thesis presents a comprehensive survey of blockchain layer 2 scaling solutions and investigates the phenomenon of market manipulation within the Bored Ape Yacht Club (BAYC) non-fungible token (NFT) collection. The emergence of blockchain technology has revolutionized various industries, and NFTs have gained significant attention due to their unique properties. However, scalability remains a major challenge for blockchain systems, limiting their widespread adoption. Layer 2 scaling solutions have emerged as potential remedies to address this scalability issue. The primary objective of this research is to provide a thorough analysis of the different layer 2 scaling solutions available in the blockchain ecosystem. A comparative study is conducted, assessing their strengths, weaknesses, and overall effectiveness in improving transaction throughput and reducing fees. The survey encompasses various approaches, including sidechains, state channels, and off-chain computation protocols. Each solution is evaluated based on criteria such as scalability, security, decentralization, and interoperability. The findings provide valuable insights into the current state of layer 2 scaling and guide future developments in this field. Additionally, this thesis delves into the BAYC NFT collection, which has gained significant popularity and market value. However, concerns regarding market manipulation within the BAYC ecosystem have arisen, raising questions about its fairness and integrity. Therefore, the study investigates potential market manipulation practices that may occur within the BAYC NFT market. It explores various forms of manipulation, including pump-and-dump schemes, wash trading, and front-running. The analysis employs data-driven methodologies, including statistical analysis and pattern recognition, to identify and understand such manipulative activities. The data for analysis is collected from reputable blockchain data sources, such as Etherscan and Moralis, including on-chain transaction records, smart contract events, and market trading data. The results of this research contribute to the existing body of knowledge in the fields of blockchain scalability and NFT market analysis. The survey of layer 2 scaling solutions provides a comprehensive overview for practitioners and researchers, facilitating informed decision-making when choosing appropriate solutions for blockchain applications. Furthermore, the investigation of market manipulation practices within the BAYC NFT collection raises awareness about potential vulnerabilities in the NFT market and suggests measures to mitigate such risks. Overall, this thesis advances our understanding of blockchain layer 2 scaling and market manipulation in the context of NFTs. It provides valuable insights into the technical and economic aspects of these topics, paving the way for further research and development in the blockchain ecosystem

Abstract

During the past few decades, the semiconductor VLSI industry has distinguished itself both by the rapid pace of performance improvements, and by a steady path of constantly shrinking device geometries. MOSFETs upon downsizing, have met the world’s growing needs for electronic devices like computing, communication, entertainment, automotive, and other applications with steady improvements in cost, speed, and area. But the device density has led to increased power consumption. Hence, Leakage, along with propagation Delay are the key metrics to evaluate the performance of any digital circuit. In nano-scale technologies(<45nm), static power occupies a significant share in the total power budget thereby prioritizing the need for leakage reduction techniques. Performance optimization of CMOS based circuits gains more significance for nano scale technology nodes. Variations in operating parameters such as supply voltage, temperature etc. have profound effects on power-delays specifications. The non-homogeneity in process parameters at such scaled technologies hampers the yield of the final designs. Performance degradation over time is another important factor determining the lifetime and reliability of an IC. In this work we develop a framework of algorithms to optimize digital circuits for low power and high performance applications. A wide range of analysis such as sensitivity of the circuit, correlation between parameters etc has been performed to understand the functioning in response to transistor sizing of digital cells. The first stage of this work aims to optimize basic digital cells through transistor sizing using the proposed optimization algorithms like Pareto Harris Hawk optimization algorithm, Glowworm Swarm optimization algorithm, Strength Pareto evolutionary algorithm-II and Neighbourhood Cultivation Genetic algorithm. The resultant system design becomes robust to withstand fluctuations caused by process, aging and operating parameters apart from providing highly improved leakage-delay performances. The second stage of this work involves optimization complex circuits. We have proposed a framework to optimize the complex circuits by selectively replacing the basic cells with the optimized sizing. The proposed framework deconstructs a given circuit into its constituent basic cells. It does a wide range of analysis such as sensitivity, the correlation between process parameters, load analysis, screening of insignificant parameters, and mismatch analysis to generate robust sizing. The algorithms generate sizing that can be used at multiple instances across different circuits. Thus the total number of design variables remains within a limit even when the cell count increases across circuits. The proposed framework then reconstructs the circuit with these optimized cells to improve the power-delay front. While replacing the nominal with the optimized sizing for each cell, the tool identifies the load it has to drive, the path (critical/ non-critical) it is present in and its driver modules in the path. The basic cells are selectively replaced with the optimized sizing using the proposed techniques: Backward traversal replacement technique and Partitioning large basic cells. Results have shown a substantial reduction in leakage power and propagation delays in addition to minimizing human effort

Abstract

Pressure sensors are popular in a large variety of industries. For some applications, it is critical for these sensors to come in a flexible form factor. With the development of new synthetic polymers and novel fabrication techniques, flexible pressure sensing arrays are more easily accessible and can serve a variety of applications. As part of this dissertation, we demonstrate one such application of the same by developing a low-cost flexible writing pad and doing crosstalk analysis on sensors with similar working principles. Digital multimedia tools are becoming increasingly important as the world slowly shifts to an online mode. We present a low-cost, flexible writing pad that uses a 16×16 pressure sensing matrix based on the piezoresistive thin film of velostat. The writing area is 5 cm × 5 cm with an effective pixel area of 0.06 mm2 . A read-out circuit is designed to detect the change in resistance of the velostat pixel using a voltage divider. A microprocessor raster scans through the sensor pixel matrix to obtain a data frame of 256 numbers. This data is processed using techniques like squaring and normalising (S&N), Gaussian blurring, and adaptive thresholding to generate a more readable output. The writing pad is able to resolve characters larger than 2 cm in length. The flexible writing pad produces legible output while flexed at a bending radius of up to 4 cm. Such flexibility promises to enhance the usability and portability of the writing pad significantly. We noticed that the raw data produced by the writing pad had a lot of crosstalk which we were subsequently able to resolve using the algorithms mentioned above. Such crosstalk has been reported in literature multiple times and is common, especially for sensors of the crossbar architecture. Crosstalk, in a sensor matrix, is the unwanted signal obtained at a sensor pixel that is not directly related to the stimulus. This paper presents a novel approach towards quantifying the crosstalk characteristics of a sensor matrix. The method involves obtaining the normalized sum of all the neighboring pixel readings, weighted by their distance from the stimulated pixel. We have used this methodology to characterise the crosstalk for a 5×5 velostat-based flexible pressure sensing matrix that uses a crossbar electrode architecture. We characterized sensor matrices with three different pitch lengths: 3mm, 4mm, and 5mm. We observe the crosstalk values to lie between 0.032 to 0.17 across different weights and pitches, which indicates a low measure of crosstalk. We observed that the 5 mm pitch matrix has the least crosstalk, which was expected due to the larger spacing between the pixels. We also characterised the crosstalk for all 25 pixels of a 4 mm pitch matrix and found the mean to be 0.081±0.002, within the range from 0.0071 to 0.1656