Abstract

The world has been experiencing rapid urbanization over the last few decades, putting a strain on existing city infrastructure such as waste management, water supply management, public transport and electricity consumption. We are also seeing increasing pollution levels in cities threatening the environment, natural resources and health conditions. However, we must realize that the real growth lies in urbanization as it provides many opportunities to individuals for better employment, healthcare and better education. Cities have now become one of the major contributors to the world’s gross domestic product; therefore, urbanization should always be seen as an opportunity. However, it is imperative to limit the ill effects of rapid urbanization through integrated action plans to enable the development of growing cities. This gave rise to the concept of a smart city in which all available information associated with a city will be utilized systematically for better city management. Needless to say, data associated to the city plays a vital role in understanding the city’s current state and quickly addressing the issues discussed earlier. The systematic collection, processing and analysis of data allow for the incorporation of data intelligence and helps inform decisions on issues that matter to the lives of city residents. City management officials have been collecting data for years; however, their department-centric nature has created data silo issues and made it difficult to manage and share, thus hindering the data analysis process. Considering these issues in existing data systems, this work proposes a novel Smart City data management system architecture to systematically collect, store, exchange and analyze data collected from various sensing nodes depicting smart city applications. The proposed system architecture is divided in subsystems and is discussed in individual chapters. The first chapter introduces and gives overview to the reader of the complete system architecture. The second chapter discusses the data monitoring system (DMS) and data lake system (DLS) based on the oneM2M standards. DMS employs oneM2M as a middleware layer to achieve interoperability, and DLS uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The third chapter discusses energy monitoring and electric vehicle charging systems developed to illustrate the applicability of the oneM2M standards. The fourth chapter discusses the Data Exchange System (DES) based on the Indian Urban Data Exchange (IUDX) framework. DES uses IUDX’s standard data schema and open APIs to avoid data silos and enable secure data sharing. The fifth chapter discusses the 5D-IoT framework that provides uniform data quality assessment of sensor data with meaningful data descriptions.

Abstract

Making things, coming up with strange new ideas is what creativity is. The question of where these new ideas come from is the very question that drives all study of creativity. Creativity has to come from the knowledge that exists in the minds of the creator [1]. This work participates in the questions of how we come up with new ideas and to what extent the conceptual space influences creativity. We form concepts through the process of abstraction. Despite having been long established that creativity does not come from the Gods but from our mental and bodily processes, there is very little work on the processes of concept acquisition. There is almost no work that directly studies creativity and abstraction together. This work argues that creativity research assumes an Amodal Symbol cognition structure where the processes of cognition and higher cognitive functions are not related. As a result this relationship has not been explored. This work employs a mixed method approach, using both qualitative and quantitative methods for coding and analysis of abstractions and creative generations. A wordlist based on familiarity, level of abstractness and modality experience was created. A norming study of the rating of abstractness of these words showed significant variability in ratings indicating subjectivity in the perception of abstractness. To study abstractness and creativity a Property Generation and an Alternate Uses task was conducted. Traditional theories of creativity assume we manipulate taxonomic or entity properties in a computer like way, during the process of creativity, however our results emphasises the role of individual experiences in the process of creativity

Abstract

Machine Translation aided workflows have become increasingly prevalent across industry and within large institutional translation efforts. We evaluate the machine translation postediting process end-to-end on three aspects: the usefulness of the process itself, as to whether it leads to reduction in temporal, technical and cognitive effort compared to unaided translation; the linguistic nature of the products of this process, in terms of the quality of translations generated measured against unaided human translations; and the suitability of utilizing these outputs towards a standard machine translation evaluation task. The language direction we study is English-Hindi, a mid to high resource language pair. We first conduct a behavioral experiment utilizing professional translators. In our analysis, employing mixed-effects modeling techniques, we find that for this language pair, post-editing reduces translation time by as much as 63%, consequently increasing productivity; it reduces technical effort measured as keystrokes logged by 59%; and reduces cognitive burden measured as reduction in number of pauses by 63%. We then investigate the nature of the translations generated during post-editing by comparing them against raw MT outputs and unaided human translations on linguistic indicators we define and implement. We find significant differences between the three corpora in terms of lexical richness, normalization and interference by the source language. We also detect and confirm the presence of translation universals in our data. We then go on to test the suitability of translated data thus created for a machine translation evaluation task. We detect engine-reference bias towards the engine utilized during post-editing, inflated engine scores across the board on post-edited references, and find the utilization of multiple references to be the most prudent choice when conducting meta-evaluation tasks. We run these evaluations across multiple string-based and pre-trained metrics and issue general recommendations on constructing evaluation test sets and metrics to score against. We also contribute a set of post-editing guidelines with examples culled from a real-world translation task for translators and post-editors working with the English-Hindi pair.

Abstract

This thesis targets the problem of surrogate approximations for similarity measures to improve their performance in various applications. We have presented surrogate approximations for popular dynamic time warping (DTW) distance, canonical correlation analysis (CCA), Intersection-over-Union (IoU), PCP, and PCKh measures. For DTW and CCA, our surrogate approximations are based on their corresponding definitions. We presented a surrogate approximation using neural networks for IoU, PCP, and PCKh measures. First, we propose a linear approximation for the naïve DTW distance. We try to speed up the DTW distance computation by learning the optimal alignment from the training data. We propose a surrogate kernel approximation over CCA in our next contribution. It enables us to use CCA in the kernel framework, further improving its performance. In our final contribution, we propose a surrogate approximation technique using neural networks to learn a surrogate loss function over IoU, PCP, and PCKh measures. For IoU loss, we validated our method over semantic segmentation models. For PCP, and PCKh loss, we validated over human pose estimation models.

Abstract

Idiomatic expressions have always been a bottleneck for language comprehension and natural language understanding, specifically for tasks like Machine Translation(MT) and Natural Language Understanding(NLU). MT systems predominantly produce literal translations of idiomatic expressions as they do not exhibit generic and linguistically deterministic patterns which can be utilized for the comprehension of the non-compositional meaning of the expressions. These expressions occur in parallel corpora used for training, but due to the comparatively high occurrences of the constituent words of idiomatic expressions in a literal context, the idiomatic meaning gets overpowered by the compositional meaning of the expression. The absence of data with a large coverage and quantity of idiomatic expressions exacerbates the issue to handling them further. Our work aims to provide a method of handling idiomatic expressions which not only suggests a pipeline for the task but also enables a process of data creation from subsequent steps which can be used in further downstream tasks. State of the art metaphor detection systems are able to detect non-compositional usage at word level but miss out on idiosyncratic phrasal idiomatic expressions. This creates a dire need for a dataset with a wider coverage and higher occurrence of commonly occurring idiomatic expressions, the spans of which can be used for Metaphor Detection. With this in mind, we present our English Possible Idiomatic Expressions(EPIE) corpus containing 25206 sentences labelled with lexical instances of 717 idiomatic expressions. These spans also cover literal usages for the given set of idiomatic expressions. We also present the utility of our dataset by using it to train a sequence labelling module and testing on three independent datasets with high accuracy, precision and recall scores. Natural Language Understanding has made recent advancements where context-aware token representation and word disambiguation have become possible to a large extent. In this scenario, comprehension of phrasal semantics particularly in the context of multi word expressions (MWE) and idioms, is the subsequent task to be addressed. Word level metaphor detection is unable to handle phrases or MWE(s) which occur in both literal and idiomatic context. State of the art transformer architectures can be useful in this context, but the absence of a large comprehensive dataset is a bottleneck. In this paper, we present a labelled EPIE dataset containing 3136 occurrences for 358 formal idioms. To prove the efficacy of our dataset, we also train a sequence classification model effectively and perform crossdataset evaluation on three independent datasets. Our method achieves good results on all datasets with F1 score of 96% on our test data, and 82%, 74% and 76% F1 score on SemEval All Words, SemEval Lex Sample, and PIE Corpus datasets respectively

Abstract

With the booming online market, managing the warehouse inventory is one of the most essential and challenging tasks. The management of inventory will be more efficient if it is automated using robots. The robots can work faster than humans, the robots work at a constant speed with no breaks, and do tasks in more repetition than humans like fetching inventory from warehouse. But for the robots to perform tasks like putting objects in racks, fetching objects from rack, re-organising the racks to make more space they need to have an understanding of the warehouse environment. To understand the warehouse environment the robots needs to create a 3D map of the warehouse consisting space to move for the robot as well as identify the racks and boxes so that its able to plan the execution of tasks. Towards solving this problem in this thesis we address problem of freespace estimation for rack shelves. Given a monocular RGB image captured from a camera mounted on a robotic arm. We aim to predict the Top-view and Front-view layouts so as to create a 3D reconstruction of rack and objects present in the Monocular RGB image. We propose a simple yet effective network architecture RackLay, which takes a monocular RGB image as input and outputs the Top-view and Front-view layout of all the shelves comprising the rack visible in the image. The Network can learn two kinds of layout representations, one in the canoncial frame centered on the shelf, called the shelf-centeric layout and the other in a frame with respect to the camera, called the ego-centric layout. Apart from portraying the versatility of the network, they lend to various useful applications. Since there are very few publicly available datasets for warehouse settings, we also introduce the synthetic data generation pipeline termed as WareSynth, which can be used to generate 3D warehouse scenes, automate the process of data capture and generate corresponding annotations. WareSynth can be used for various tasks such as 2D/3D object detection, semantic/instance segmentation, layout estimation, 3D scene navigation and mapping, 3D reconstruction etc. The same pipeline can also be modified to other kind of scenes such as supermarkets, greenhouses by changing the database of objects and placement parameters hence this pipeline open gates for further research in similar environments.

Abstract

Blockchains lie at the heart of Bitcoin and other cryptocurrencies that have shown great promise to revolutionize finance and commerce. The novel applications of blockchain technology are derived from the unique combination of properties of blockchains: immutability, incorruptibility, consensus, transparency, decentralization and ordering. However, they face technical challenges when it comes to scaling to support greater demand while maintaining their desirable security properties. In an exciting line of recent work, many researchers have proposed various scalable blockchain protocols that demonstrate the potential to solve these challenges. However, many of these protocols come with the assumptions of honest majority and symmetric network access which may not accurately reflect the real world where the participants may be self-interested or rational. In this thesis, we work towards analysing blockchain protocols in settings with rational participants and use game-theory to predict their behaviour. For the first time in literature, we study blockchain protocols in a setting with asymmetric network access and highlight the role of network fairness in ensuring the security of a blockchain protocol. We demonstrate via simulations, the effect of lack of network fairness in Bitcoin and the equilibrium behaviour of the participants which results in loss of both security and performance of the protocol. In order to further highlight the importance of network fairness in designing scalable blockchain protocols, we showcase incentive-driven deviations in the OHIE blockchain protocol proposed recently and the loss of security caused by these deviations. We then study a class of blockchain protocols designed by layering two distinct types of consensus protocols and determine the parameters required to ensure game-theoretic soundness and security. We also formally study the reward schemes employed by Proof-of-Work-based blockchain protocols and showcase the lack of game-theoretic soundness. Towards this, we also propose a class of game-theoretically sound reward schemes. Finally, we extend our work by developing a model for layered-blockchain protocols and present a game-theoretic model for analyzing the security of layered-blockchain protocols. Therefore, in this thesis we present novel work that aims to guide the development of fair and game-theoretically sound blockchain protocols.

Abstract

Natural language processing (NLP) is a bridge between the computer and human interactions in their natural language. NLP has wide variety of applications such as machine translation, text summarization, question-answering, sentiment analysis, etc. All these applications have created huge impact in the society with different use cases such as chatbots, voice assistants (Alexa, Siri), recommendation systems (YouTube, Hotstar) etc. Most of these NLP applications are limited to few high resource languages like English. Notably, in an Indian scenario, where each state is having its own language, only 10% of people communicate in English. This motivates us to develop NLP systems and resources in Indian languages that will create a huge impact in multilingual Indian society. In this thesis, we created question-answering (QA) and text-summarization resources and systems in Telugu language. The main aim of QA system is to provide an accurate and concise answer to the question asked by human in natural language. In this thesis, we created a question classification dataset which consists of 1037 samples and also explained the ambiguities, challenges involved in creating the dataset. We built an end to end pipeline for QA system and named it as AVADHAN. We performed comparisons between three different classifiers for the Telugu Question Classification (QC) module. QC will be helpful to reduce the search space while extracting the answer for the given query. Text summarization is a way of obtaining short and precise summary from the given document of arbitrary length. In this thesis, we have proposed a pipeline that crowd-sources summarization data and then aggressively filters the content with automatic and partial expert evaluation. With this pipeline we have created TeSum: high quality human generated abstractive summarization corpus for Telugu. This corpus consists of 20329 high quality articlesummary pairs and this is the first high quality and large abstractive summarization corpus in Telugu as per our knowledge. We have also explored different sequence to sequence neural networks on TeSum corpus and provided ROUGE scores.

Abstract

Since the 1964 Niigata earthquake (the epicentre is located at the continental shelf off the northwest coast of Honshu, Japan), dynamic soil-structure interaction has been considered an important factor in many important structures such as tall buildings, bridges, nuclear power plants, etc. As soil-structure interaction analysis is a complex phenomenon, researchers have developed different techniques through experimental, analytical, and numerical approaches. Amongst all the techniques numerical methods are found more reliable in the design of structures to include the effects of soil-structure interaction. However, radiating waves from structure are one of the major concerns in numerical modelling of the soil-structure interaction. To solve the radiating wave propagation problems using finite element analysis (FEA), it is required that the boundary must be terminated at some finite location. This truncation of the model at the finite boundary will cause the reflection of radiating waves. The reflected waves from the boundary will affect the solution and may lead to instabilities in the numerical analysis. Therefore, it is necessary to provide an artificial boundary condition that will transmit the outwardly propagating waves with minimal or negligible reflections. The primary objective of this research is to develop an efficient radiating boundary condition for numerical simulation of wave propagation in nonlinear, unbounded spatial domains. Despite several attempts by the researchers, the challenge of developing a computationally efficient absorbing boundary condition (ABC) to resemble the Sommerfeld radiation condition has not been well addressed. Absorbing Boundary Conditions (ABC), also called Local Absorbing Boundary Conditions are simple and computationally efficient, but they produce spurious reflections when the wave impinges on the boundary in a direction other than the normal. Absorbing layer techniques are efficient in absorbing outwardly propagating wave energy, but these techniques require many layers. Researchers have also attempted to combine the Absorbing Boundary Conditions (ABC) with the Layers by Increasing Damping (ALID) to utilize the advantages of both methods. Since ABC is only applicable to wave propagation in elastic media, the attempt to combine the two techniques becomes unsuccessful due to an impedance mismatch. In this thesis, a new absorbing boundary condition for wave propagation in a viscoelastic medium (VABC) is proposed. The method is an extension of the standard ABC proposed by Lysmer and Kuhlemeyer (1969). The proposed method does not converge to Kelvin-type viscoelastic materials but can be applied to Maxwell-type viscoelastic materials, i.e., only mass proportional damping is considered. The accuracy of the method is studied for viscoelastic wave propagation problems, and the results are compared with the standard ABC and analytical solutions.  The analytical and numerical results show that the VABC boundary conditions are promising in absorbing the wave energy when the damping ratio is less than 20% and produces the reflections when damping ratio is more than 20% due to dropping the higher order terms in the expansion. The VABC produces spurious reflections when the waves are not impinging in the normal direction, and reflections increase as the angle between the wave propagation and the normal direction increases. The study extends to provide an efficient absorbing method by combining VABC boundary conditions with Absorbing Layers by Increase in Damping (ALID). The main objective of ALID is to attenuate the reflected waves from VABC in cases of angle incidence. The combination of ALID and VABC, i.e., ALID+VABC, is achieved by matching the impedance of VABC with the last layer of ALID. Results from ALID+VABC are compared with other methods such as ABC, ALID, and SRM (Stiffness Reduction Method). A sensitivity analysis is carried out to verify the efficiency of absorbing the propagation wave energy at various loading frequencies. ALID+VABC has been found to be numerically efficient across a wide range of loading frequencies when compared to the other methods. The method also requires shorter absorbing region lengths, which allows for a smaller number of absorbing layers. However, all the absorbing layer methods such as ALID, ALID+VABC, SRM, and PML are poor at allowing smooth propagation of the wave through layers when waves are entering at a higher incident angle. Dynamic Soil-Structure-Interaction analysis is carried out on a three-dimensional tall building with 20 stories using ABC, ALID, and ALID+VABC as a radiating boundary condition. The complete Soil-Structure-Interaction (SSI) analysis is carried out in two stages. First, a nonlinear static analysis is carried out with gravity loading. The absorbing layers in ALID and ALID+VABC were also present in the static analysis since the damping properties did not influence the analysis. Later, nonlinear dynamic

Abstract

We design a deep learning-based AI algorithm for analysing paediatric patients’ optical coherence tomography (OCT) scans. Our system can distinguish normal from abnormal paediatric retinal OCTs in binary and six-group classification, followed by a ten-layer segmentation of retinal OCT scan and nystagmus detection by performing gaze tracking. Our AI system successfully differentiated normal and abnormal scans with 97.68 % accuracy. Furthermore, the six-point classification system (normal, grade 1-4 FH and atypical FH) achieved a 93.54 % validation accuracy. We have demonstrated the use of AI in classifying paediatric OCT, which can help in the automated diagnosis of abnormal retinal development like Nystagmus at an early age. To extend our work, we introduce segmentation of the ten retinal layers of the central scan. Since individual layer thinning or thickening may be markers of retinal disorders or precursors to future visual loss; hence segmenting individual layers and location-specific measuring their thicknesses is critical in therapeutic practice. By implementing the state-of-the-art DeepLabV3 model, we aim to achieve fully automated segmentation of each of the ten layers of retinal OCT scan. We use the ten carefully segmented ground truth data of the patients, including left and right eyes. We have achieved 0.834 as our mean IoU and 0.896 as our Dice Score on the best-performing DeepLabV3 ResNet-50 semantic segmentation model. Our model accurately predicts all ten layers of the retinal OCT scan, which would be utilized for calculating layer thinning or thickening in later stages of the project. We can also determine regional thickness, brightness, or texture-based indices of individual layers by retinal layer segmentation. This work on layer segmentation contributes to our understanding of retinal or optic nerve head (ONH) disease processes and is used to assess disease status, treatment responses, and visual function, among many others. Our Gaze Tracking model helps with nystagmus waveform generation. Nystagmus is characterised by rhythmic, abnormal eye movements that begin with a ”slow” movement that moves the eye away from the target and ends with a second movement that returns the eye to the target. Horizontal, vertical, torsional, or a mixture of these movements can be present. Hence to tackle such a problem with machine learning, we design an algorithm that could replace the Gold standard Eyelink II that is used to get the waveform for Nystagmus disease. Our method can properly generate such waveforms with high accuracy. This algorithm might get extended to replace the expensive machinery used in current ophthalmology labs. The video data used to test our model has been taken via mobile phones, which further assists in replacing the need for physical presence at the clinic for the diagnosis.