Abstract

Internet of Vehicles (IoV) is an extension of Vehicle-to-Vehicle (V2V) communication network. IoV is a connected adhoc network (Vehicular Ad Hoc Network (VANET)), where each vehicle in the network is a node, connected to other vehicles and also to the public Internet. IoV helps in enhancing driving aids with the help of vehicle’s Artificial Intelligence (AI), awareness of other vehicles and their actions. The autonomous vehicles can instantaneously communicate with other vehicles surrounding them. Since the communication among various entities involved in the IoV environment is via open channel (e.g. vehicles, pedestrians, fleet management systems, and road-side infrastructure), it gives an opportunity to a passive/active adversary to intercept, modify, delete or even insert fake information during communication. It is then a serious concern for the vehicles users to determine whether the received information is genuine. Many security protocols have addressed this issue by adding authentication mechanism. Authentication of sender is a process of verifying his/her identity claimed by or for a system entity. Authentication of the messages ensures that the messages which is flowing in the network is unintercepted and unforged. Mutual authentication is a type of authentication which allows the involved entities simultaneously authenticate each other in order to establish a secure communication between them via computed session key. In this thesis, we study the IoV paradigm and its comparison and evolution over VANETs. We present a taxonomy of security protocols designed for IoV security. We address the threats and attacks in IoV environment. In particular, we focus on various authentication protocols in IoV. We propose authentication schemes, to accomplish mutual authentication, conditional privacy preserving, batch authentication and blockchain based authentication among the involved entities in the IoV environment for secure communication. For every proposed scheme, a detailed comparative analysis among various state-of-art authentication protocols proposed in the related IoV environment is provided to show their effectiveness as well as security and functionality features. The first study presents a new mutual authentication and key agreement protocol in Internet of Vehicles-enabled Intelligent Transportation System (ITS), using the elliptic curve cryptography (ECC) technique. The proposed protocol deals with the challenges during open communication among entities in IoV environment. An open communication can be targeted by the adversary to eavesdrop, modify, insert fabricated (or malicious) messages, or delete any data-in-transit; thus, resulting in replay, impersonation, man-in-the-middle, privileged-insider, and other related attacks. In addition to providing security, the proposed scheme also achieves anonymity and untraceability. It provides a mutual authentication between vehicle and vehicle and also between a vehicle and a road side unit (RSU). Using both formal and informal security analysis, as well as formal security verification using an Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, the proposed scheme is proved to be secured against several known attacks in an IoV-enabled ITS environment. Furthermore, a detailed comparative analysis shows that the proposed scheme has low communication and computational overheads, and offers better security and functionality attributes in comparison to other competing schemes. We also evaluate the performance of the proposed scheme using NS2. In our second study, we design a new conditional privacy preserving batch verificationbased authentication mechanism in the IoV environment using ECC technique, where a vehicle can authenticate its neighbor vehicle and also an RSU can authenticate its nearby vehicles in a batch. The proposed scheme incurs low computation cost as it is implements batch authentication. The proposed scheme is shown to be highly secure against a passive/active adversary through various security analysis, such as random oracle based formal security, formal security verification via automated simulation tool (AVISPA), and also informal security analysis. An exhaustive comparative analysis reveals that the proposed scheme offers better security and functionality attributes, when compared with the relevant schemes. Finally, in the third study, we focus on designing a novel blockchain-enabled batch authentication scheme in Artificial Intelligence (AI)-envisioned IoV-based smart city deployment. We collaborate mutual authentication, batch authentication and blockchaining to produce an efficient scheme. The vehicles in IoV can be used to opportunistically gather and distribute the data in a smart city environment. However, at the same time, various security threats arise due to insecure communication happening among various entities due to lack of trust. Moreover with the increase in number of vehicl

Abstract

In the first part of this thesis, we analyze a multi-cloud marketplace model consisting of brokers with multi-unit demands and cloud providers with multi-unit supplies. Using tools from cooperative game theory we show the existence of a core allocation which also maximizes the market surplus. We do this by finding an optimal coalition structure and formulate an integer linear programming problem to maximize the overall market surplus. The optimal coalition lies in the core which gives it stability and no incentive to deviate to any other sub-coalition. We then study our model in the real-world scenario in which the brokers don’t reveal their valuations for the cloud providers. For this, we use the auction algorithm due to Bertsekas [2] which leads to a core allocation without requiring the brokers to reveal their utilities. In the second part of this thesis, we study how an oligopolist influences the coalition structure in federated cloud markets. We consider two price offering strategies for an oligopolist: non-adaptive and adaptive. In the non-adaptive strategy, an oligopolist makes a price offer to all the cloud providers simultaneously. It can be noted that the oligopolist can buy-out all the cloud providers by making a price offer which is equal to a core allocation and the total price offer made by the oligopolist is equal to the value of the grand coalition. In the adaptive strategy, the oligopolist approaches the cloud providers one after another in a sequential manner. We show that by using the adaptive strategy, the oligopolist can buy-out all the cloud providers at a total price offer which is less than that of the non-adaptive strategy

Abstract

Plastic optical fibres (POF) can be made sensitive to various parameters. Therefore, a implementation of tomographic imaging based on POF sensors is a good application in pressure sensing domain. The system uses photons to transmit information along the sensor and deliver a signal at a detector of the sensor, this Photonic Guided Path Tomography (PGPT) is used to design carpet. It is found that the POF based pressure sensing platform has several limitations such as photodiode output voltage variations, difference in the sensor response for horizontal and vertical fiber and acquiring data at unexpected intervals. Optical pressure sensors also suffer from the drawback of instability due to LED, photodiode, POF, or all. These limitations can lead to incorrect estimation of output voltage. Stabilization of output voltage is an essential factor to be taken into consideration. To remove such limitations and improve pressure imaging of floor sensor carpet for clear contrast is a big challenge in the optoelectronics systems. To surpass the above limitations, we have developed time based calibration using time windowing technique to reduce the standard deviation of photodiode output voltage variations by 98%. We have also described space and weight calibration techniques based on the convex and concave shape of fiber bend. These techniques help in improving the Landweber image reconstruction algorithm to obtain significant clarity and improvement in object pressure images. The major work demonstrates time, space and weight based calibration techniques for pressure monitoring in the low-cost plastic optical fiber (POF) sensor carpet. The proposed work also reports an artificial intelligence (AI) based algorithm to determine the accuracy of positioning of the load. Experimental results demonstrate that this algorithm gives a mean square error of 0.875 cm in position detection on the carpet. The proposed work also done using IoT application, to get real time data in remote sensing. Here, Message Queuing Telemetry Transport (MQTT) and ThingSpeak server used for communication. The images constructed using wired communication and remote sensing are almost similar in terms of quality and information. The work discusses the potential for a compact and cost-effective pressure sensor carpet, which integrates with the living environment and the outside world.

Abstract

Determination of seismic safety of existing buildings is a time consuming and challenging process. Instead, rapid visual survey methods were developed which also identify deficient structures from a large building stock in a city or town. These rapid visual screening (RVS) methods are quick and require minimal time. The important part of any RVS method is identifying critical architectural and structural features, i.e., irregularities (also known as vulnerable parameters). The score value assigned to each vulnerable parameter plays a vital role in deciding the risk of the building. Therefore, it also matters significantly that how these score values are derived. In the last two decades, several RVS methods were developed by many researchers and civil engineering professionals in India. The study in this thesis begins with a comparison and critical review of existing RVS methods used for seismic assessment of existing reinforced concrete buildings. The study focuses on rapid visual screening methods developed for the safety assessment of reinforced concrete buildings in the Indian subcontinent and a widely used method in the United States. The comparison is carried out in various ways. Initially, a direct comparison is made based on vulnerable parameters and damage grades proposed by each method. Later, a scoring system is developed to highlight the differences and rank the selected methods. It was observed that there are many uncommon vulnerable parameters amongst each selected method. Later, to understand the workability of each selected RVS method, a rapid visual survey was conducted on 100 reinforced concrete buildings in three different cities (viz., Pithoragarh, Gangtok, and Agartala) in India. The results of all five methods were found to have different outputs with significant variation for the same sample of surveyed buildings in each city. From investigation on such varying results, it was observed that relative weights assigned to each vulnerable parameter are different in each RVS method. Therefore, it was felt necessary to fix the relative weights of each vulnerable parameter to have unanimity in the results of different RVS methods. The present study attempts to derive the relative weights of the few selected vulnerability parameters on overall vulnerability of reinforced concrete moment resisting framed buildings using a numerical approach. To determine the relative weight of any vulnerable parameter, it is necessary to understand and effectively quantify its effect on the global response of the building. The quantification of the impact can be determined with the use of an appropriate damage index. The study presents a brief discussion of currently available and widely used damage indices along with their pros and cons. From the literature study on damage indices, the most suitable index with some modifications is adopted in this study. As a modification, the study proposes the hinge state coefficients and the procedure to derive the same. Thus, the vulnerability index can be determined using these hinge state coefficients and the total number of hinges formed in a structure. To derive the relative weights, the inelastic performance of irregular frames is compared with the performance of regular frames. The irregularities (i.e., vulnerable parameters) considered in this study are old building (i.e., building designed with previous code), open ground storey, floating column, and the taller floor. Nonlinear static analysis is performed on all regular as well as irregular frames to study the inelastic behaviour. The regular frames and irregular frames are initially compared based on the results of nonlinear static analysis and later based on calculated vulnerability index values. The vulnerability index values are determined at the global level and storey level as well. The comparison based on capacity curves and hinge patterns show a drastic change in irregular frames. Finally, a statistical analysis of the thypothesis test on data sets of regular and irregular frames was used to estimate the relative weights of selected vulnerability parameters. The results show that the floating column vulnerable parameter has the worst effect on global damage of structure for infill frames, followed by the open ground storey, building designed with previous code, and taller floor.

Abstract

RNA is a versatile biopolymer. Different RNAs build up the protein synthesis machinery in living organisms. Non-protein coding RNAs often participate in gene regulation, catalysis of biochemical reactions, and many other cellular processes. The efficiency of functional RNAs depends on their accurate folding into compact three-dimensional structures. RNAs are composed of only four types of nucleotides having similar chemical properties. So, the sources of structural complexity observed in these folded RNAs remain ambiguous. Instead, hydrogen-bonded planar RNA base pairs have the potential to be the fundamental building block of RNA. They display remarkable diversities in their geometries, stabilities, and physicochemical properties. Properties of RNA base pairs are strongly influenced by nucleobase modification, such as post-transcriptional changes, tautomerization, ionization, etc. One such important modification is nucleobase protonation. Protonation at different polar sites of nucleobases are associated with positive free energy change. However, protonationinduced modifications to nucleobases result in subsequent stabilizing interactions, which compensate for the thermodynamic cost of protonation. For example, protonation converts a hydrogen bond acceptor to a hydrogen bond donor and therefore opens up new base-pairing opportunities through the protonated edge of the nucleobase (Class I). In other cases, the protonation-induced changes may influence the stability of the base pairs formed through the unprotonated edges of the base (Class II). Instances of Class II protonation in RNA bases is nontrivial to detect for both experiments and in-silico techniques, as the loaded proton does not form any inter-base hydrogen bonds. Hence, the contribution of Class II protonation in determining RNA’s structure and function remains majorly unexplored. Recently a strategy has been proposed to study the consequences of Class II protonation on the geometry and stability of RNA base pairs (1). It involves comparing the electronic structure properties of the different base pairs optimized with and without the Class II protonation. In the past, this method revealed that Class II protonation at the N7 and N3 positions of guanine stabilizes different RNA base pairs, which are intrinsically unstable. In this work, the same strategy is applied to explore the consequences of Class II protonation at the N3 position of guanine and N3 & N7 positions of adenine on the geometry and stability of different RNA base pairs. Those are the sites where the thermodynamic cost of protonation is higher than other sites. Analysis of RNA crystal structure datasets and electronic structure calculations carried out at DFT, and DFT-D levels reveal a number of interesting insights. In general, it is observed that charge redistribution caused by Class IIprotonation alters the hydrogen bonding potential at the polar sites located on the unprotonated edges of nucleobases. Such changes collectively determine the geometry and stability of the base pairs formed through the unprotonated edges. A set of RNA base pairs are reported which are sensitive to Class II type nucleobase protonation and hence have putative biotechnological application in pH sensing.

Abstract

Summarization involves reducing the size of the document without the loss of important content. The reduced content is called the summary of a document. Summarization can be divided into two broad categories: Extractive and Abstractive. In Extractive Summarization, the summary of a document is constructed by extracting phrases and sentences from the document itself. In this work, we are mainly concerned with single document extractive text summarization and employing deep-neural-networkbased solutions for the same. While on the surface, extractive summarization seems like a trivial problem since summarizing here involves copying content from the source document, in reality, it is far from trivial. One main reason for the complexity of the task is the absence of gold labels for what sentences/phrases to extract. Typically, extractive summarization systems are evaluated using human-written summaries. However, this summary cannot be directly used to train the neural networks. Indirect methods of labeling or training are commonly used in many of these cases. Extractive Summarization can be divided into two steps: Document Understanding and Document pruning. A document is not simply a bunch of sentences put together. There is an underlying structure between the way sentences are arranged, which makes a document coherent and meaningful. Moreover, summarisation can be said to utilize this exact structure (among other things) to determine which sentences are more important and relevant. Most works assume that the structure of the document is linear and use recursive networks to encode them. But it was shown by Rhetorical Structure Theory (RST) that documents can be represented by tree-like structures. One of the first things we explore is to see if we can use non-linear tree-based networks to encode the document. Moreover, since learning to summarize involves learning to understand documents implicitly, we see if we can try to capture such structures implicitly when learning to summarize. We also explore if such structures can aid in summarisation. Another reason for the complexity of the task is that extractive summarization is different from sentence ranking. Sentence ranking can be defined as ranking sentences based on how good a sentence is for representing the document. However, in summarisation, the selection of each sentence also depends on the other sentences selected. This makes the task very complex due to the exponential number of possibilities in selecting a subset of sentences to represent the document. Each of these possible subsets of sentences can be called a candidate summary of the document. A document of length 25 has over two thousand such candidate summaries, only when considering those of length 2 and 3 sentences. This creates a combinatorial explosion problem that we should overcome by usnig indirect means. Many works deal with this combinatorial problem in their own way. Here we also explore different novel and unique methods for doing the same. We introduce a new pipelined architecture for extractive summarization, which helps in dealing with precisely this problem. We also see how we can adopt different complex reinforcement learning (RL) formulations to extractive summarisation. Many works use one of the two formulations in training their models: either policy-gradient-based algorithms or deep-Q-networks. In this work, we see how we can improve both of them. We also experiment with neural-network-based loss functions for summarisation. At last we see the problems that can arise in training extractive summarisation models when we try to circumvent the combinatorial explosion problem. Many works try to simplify their training objective by approximating summarisation as sentence classification, or use very limited set of samples to train their models. This creates a lot of issues in what the model actually learns. We show this by experiments and analysis, and also provide a glimpse at a solution for solving the same.

Abstract

This thesis revolves around two questions. How have the ideological positions of political parties in India evolved in the 21st century and what changes are these bringing to the participation of people in democratic processes? Literature on ideology and participation are discussed in the initial part of this thesis. It uses election manifestos published by political parties for the elections from 2004 to 2019 to identify the ideology of political parties. A manifesto coding scheme adapted to Indian conditions was used to categorize manifesto points to different heads based on ideological leaning. The evaluation suggested a shift towards capital and private entrepreneurship, increasing focus on urban areas, changing attitude towards decentralisation depending on the power relations. Political parties showed a left leaning view on social affairs while positions varied from central right to left on economic and political affairs. The political parties are becoming more inclusive of different sections of the country though the trend is not consistent. To identify participation, the focus was given to Muslim participation. Detailed election results published by the Election Commission of India from 1977 to 2019 was used to identify participation in elections. Muslim names were identified using a code. geographical distribution was mapped using QGIS. Muslim participation was increasing consistently from 1991 to 2014 before dropping in 2019. The participation is largely through recognized political parties than as minor groups or independents. Muslim identity based political parties and Islamic parties are trying to spread the influence geography wise and vote wise. But they remain minor players in relation to Muslim participation where recognized political parties which are not based on Muslim identity fields more number of candidates.

Abstract

Research in Natural Language Processing is expanding in multiple languages and is seeping into all domains of life with time. The variety of text which can be processed is growing with every advancement. Online social media platforms have become a rich source of textual data for several NLP tasks. One such social media platform is Twitter. In the present day, tweets on Twitter have become vital for understanding humans. They should be analyzed to obtain more relevant information, such as sentiments and emotions. On the other hand, the massive growth in communications on these sites by users across the world has its ill effects. Many individuals are taking undue advantage of these platforms to post aggressive and hateful content on other individuals and groups. The amount of content that resembles this form has increased beyond the scope of manual filtering for any research group/organization. Hence, the availability of automatic identification systems and annotated datasets in this domain is of utmost importance. From many years, a phenomenon called “code-mixing” has attracted a lot of research and interest from sociolinguists. According to Wikipedia, Telugu is the fifteenth most-spoken language in the world. Telugu is observed to be mostly written in Roman script on social media platforms. Code-mixed Telugu-English content is growing digitally by people residing across the globe. This thesis focuses on detecting two elements, i.e., aggression, and hate speech from code-mixed Telugu-English online posts, which are two different text classification problems. Currently, there are no publicly available datasets in Telugu-English for these tasks. In our attempt to create data resources for these tasks, this work presents a corpus of 3677 and 3361 tweets annotated with aggression and hate speech, respectively. These datasets are of great value as all annotations are done manually by two annotators, which makes them very rich for the research community. In this thesis, we describe the creation and annotation process, data, and the possible uses of the datasets. We have also built a baseline classification system to identify hate speech and aggression using the corpus created. The annotations have been used for training and testing the classification models. This work gave us an opportunity to work on code-mixed Telugu-English data, which is a relatively less explored field.

Abstract

This thesis revolves around a rather simple question - Is Sport Art or Not? We discuss some of the scholarly works that debate around this question before we charter our own course of answering this question. We engage with this discourse by keeping Sport at the centre of our analysis. We discuss the Idea of Sport and the concepts that are central to it before analysing the structure of Sport.To analyse the Idea of Sport, we study the historical transformation of Play and how it degraded to be something else. We also analyse social structures that replicate the structure of a Game to further understand the position of Sport relative to Play and Game. For analysing the structure, we selected the following sports - Track, Hurdles, Shot Put, Long Jump, Bowling, Weightlifting, Pacman, Badminton. These sports were selected as they display a serial logic in how their events are arranged. We construct the graphical representation of these sports by listing out the events and the rules that render the events their serial causality.We propose a method of construction grammars for serializable sports and provide a prototype of a classification of sports based on the grammar generated. Based on our observations of the above mentioned exercise, we arrive at a conclusion that Play leads to Art and that Sport is not Art.

Abstract

There is an explosion of information in the digital world. In a crowded digital world, recommender systems are a prerequisite for any digital media platform that serves content to users of varied interests. Platforms like Netflix, Amazon, Youtube have leveraged and contributed to the extensive work in recommender systems and are continuously improving the status quo. In the last decade, another kind of content-based platform has emerged – the EdTech kind. Though having the same need, to serve relevant and engaging content to its users, this domain however has very distinct nuances that make the traditional recommendation systems and the theory, metrics and impact factors around it misfit for its use case. For example, it makes sense for youtube to recommend a jingle that a user keeps listening to, it makes sense for netflix to recommend an all time favourite episode from a sitcom. Numerous experiments have shown that this infact helps to boost the users engagement on the platform. This approach does not work for EdTech where the intention is to have the user consume optimal content for knowledge gain. Repeat recommendations will in fact fatigue the user and drive him/ her off the platform. Another key nuance that we make a central theme in the thesis is the concept of ‘knowledge gain’ and posit that recommender systems for eLearning have to optimise this as a parameter. This is something that is unique to eLearning only and needs a careful thought. Overspecialization in the recommender systems is a big problem for edtech as it inhibits the users learning curve and ultimately reflects poorly on the edtech platforms catalogue and user journey. Thus, a healthy amount of knowledge gain injected by appropriate recommendations goes a long way in the eLearning domain. The limitations of the current ecosystem propelled us into an inquiry on the exact nature of these gaps. Building on, we propose a content based recommendation algorithm - ‘KNN-RkNN’. Inspired by the theory of active learning, our ‘KNN-RkNN’ (K Nearest Neighbour - Reverse K nearest neighbour) algorithm is optimised for information gain that’s learnt from the users history. The data was sourced from a popular EdTech platform, Unacademy. The effectiveness of this algorithm was measured against relevance, diversity and NDCG (Normalised discounted cumulative gain). This algorithm was tuned in perspective of optimising for knowledge gain. We show that taking informativeness as a feature, independent of the diversity and relevance, can lead to an unbiased exploration of good content in a large content repository. This thinking is a new line of thought in edtech and paves way for further research on development of recommendation algorithms primed on informativeness as a feature.We also research on building an enhanced sequential collaborative filtering technique that learns from the users watch history and recommends a learning path/playlist. This recommendation engine consists of a seq-2-seq model with multiple LSTM layers with attention mechanism. The results of this experiment are encouraging. An accuracy above 90% is achieved when the number of classes to recommend are reduced. Different experiments with similar architectures yielded different results which have been documented in this study. The chief contribution of this research is to qualitatively establish how knowledge gain when considered as a objective for eLearning is a nuance that was missing in eLearning thus far. We also quantitatively establish this via multiple case studies. Byproduct of these studies is to demonstrate how knowledge gain can be incorporated in both content based and collaborative filtering techniques. A more subtle contribution of our work is its lab to market nature. The sequential collaborative learning algorithm explained in the thesis is industry ready both for performance and scale. This research hopes to serve as a base for future studies to tune knowledge gain not just in content but also in other eLearning aspects like polls, tests, discussions etc.