Abstract

Ring laser gyroscopes (RLG) are optical inertial rotation sensors used to measure the rate and direction of rotation. With the help of accelerometers, they provide accurate information about the position and orientation of an object. Their sensitivity depends upon the intended application: military navigation requires gyros with sensitivity of 0.01-0.1 deg/h while automobiles and handheld application need sensitivity of 1-10 deg/h. As the sensitivity of gyro is directly proportional to its size, the high performance military applications are dominated by the bulky He-Ne RLG. Over the past few decades, strong theoretical research backed up by advanced fabrication technologies have improved the performance of He-Ne RLG significantly. In the era of on-chip integrated optical devices, large size and high power requirement of He-Ne RLG limits its applications. This is where Semiconductor RLG (SRLG) provides a viable alternative. SRLG is a compact, low cost and low power inertial rotation sensor working on the principle of Sagnac effect. It offers the promise of fabricating the complete gyro system on a single Photonic Integrated Circuit (PIC). Many implementations of bulk-optic and integrated SRLG have been proposed, but their reported performance has been highly inferior to He-Ne RLG. While bulk-optic SRLG has shown sensitivity of 10 3 deg/h, the reported sensitivity of integrated SRLG has been 10 8 deg/h, which is unacceptable even for low performance applications like automobiles. While the poor performance of integrated SRLG has been attributed to phenomena like mode coupling and gain competition, a detailed performance analysis has not yet been reported. Hence, critical performance limiting parameters could not be identified and feasible practical solutions to enhance the performance could not be proposed. This has inhibited the development of SRLG technology towards high performance applications. In this thesis, SRLG has been mathematically modeled using rate equations of counter-traveling electric fields inside the gain medium and the resonant cavity. The Sagnac beat signal obtained by simulating the model is verified by rotating the experimental setup of the gyro. The sensitivity, which is found to be limited by locking of the counter-traveling fields, is enhanced by proposing few novel designs and biasing techniques. Although these techniques improve the sensitivity of SRLG, the overall performance is still very poor compared to the military navigation standards. In order to identify the critical performance limiting parameters and phenomena, every metric of SRLG such as quantum limit, angle random walk, scale factor stability, null shift and lock-in threshold have been thoroughly modeled in terms of material, geometry and environmental parameters. Moreover, effects of nonlinearities such as spatial hole burning, mode coupling, gain saturation etc. on the SRLG sensitivity have been evaluated. The critical performance affecting parameters are identified and optimized to achieve best possible performance. For integrated SRLG, lock-in due to non-linear backscattering inside the semiconductor gain medium is found to limit the sensitivity to 10 8 deg/h. Use of external coupled-cavity semiconductor ring lasers and stimulated Brillouin scattering (SBS) based ring lasers as gyroscopes are proposed for high performance military applications.

Abstract

A legal judgment is a document which provides an explanation of the court order concerning a legal case. The legal data is being digitized and is being made available in digital format for access by legal practitioners and the general public. Also, the legal data is continuously growing with the judgments being delivered. In common law based legal systems, legal practitioners refer to the previous judgments of the court to prepare the new judgment. Extracting relevant legal judgments for a given input judgment is useful to legal practitioners. The inherent complexity of a legal judgment requires effective and efficient ways to extract relevant legal judgments for a given input judgment. Legal judgments contain text and citation information which can be exploited for finding relevant legal judgments for a given input judgment. In the literature, efforts have been made to exploit the text and citation information for finding similarity between the pairs of judgments. In this thesis, we show the potential of citation information in the judgments, in finding similar judgments through cluster analysis and for ranked retrieval of relevant legal judgments. Firstly, we analyzed how the citation information could be helpful in finding similar legal judgments. Link information has been exploited extensively in the web domain for search and retrieval systems. Similarly, citation information in the legal judgments can be exploited for building efficient search systems in the legal domain. We developed a clustering approach to cluster judgments having only citation information. We have conducted experiments on real world legal judgment dataset delivered by Supreme Court of India. The clustering results indicate that the citation information is helpful in finding clusters of judgments. As the naturally available citations are sparse, we also exploit the notion of paragraph links to increase the number of citations in the judgments and reported the corresponding clustering results. Further, we have made an effort to show the potential of citation information in the ranked retrieval of relevant legal judgments for a given judgment. Efforts are being made to build efficient search systems that extract legal judgments for a given input query by extending the traditional information retrieval approaches. Intricate legal concepts are discussed at a granular level in the legal judgment documents. In addition to citation information, we exploit the paragraph-level information of the judgments for ranked retrieval of relevant legal judgments for a given input judgment. Instead of considering the entire text document for comparison, we find the aggregate relevance between judgments by computing the relevance at paragraphs of the judgments based on the Okapi retrieval model. User evaluation study on the real world legal judgment dataset shows that the citation based approach improves the performance by combining with Okapi retrieval model. Overall, we conclude that citation information available in the legal judgments can be exploited for finding similarity among legal judgments.

Abstract

Recently representation learning has been receiving much attention from both industrial and academic communities. Thanks to the advent of powerful GPUs and advanced techniques like dropout and rectified linear units, the interest in neural networks has been revived. The current state-of-the-art solution for the applications in the field of Natural Language Processing (NLP) and Information Retrieval (IR) is powered by neural network based representation learning models. In this thesis, we focus on the unsupervised representation learning models which are cheap to build (as they rely on unlabeled data) but very effective for many downstream applications. We explore two main challenges in building an unsupervised representation learning model for NLP and IR problems. Context serves as the source of knowledge for estimating the representation of data. For example, in Word2Vec, the context is the set of words surrounding a given word in a sentence. The first challenge we focus is the context insufficiency problem. For instance, consider the models used in practise to generate representations for tweets. We observe that tweets do not exist in isolation, and hence the performance of the models which work only with the content of the tweet (as context) is found to be sub-optimal. To handle this issue, we propose a better model which also captures the interactions between the tweet and its adjacent tweet (as context) in the users’ timeline. Along similar lines, we explore two more use cases where we smartly incorporate novel contexts that captures complex interactions such as scientific author and his/her paper interaction in an author collaboration network (‘Author2Vec’) and sentences interaction in a document (‘Doc2Sent2Vec’), that turned out to be advantageous in computing accurate author and document representations respectively. We conclude that if we smartly leverage the available contexts, the performance of the model improves significantly. Though the representation learning models perform well in practise, little is known about the core properties of the data encoded within the representations. Understanding these core properties would empower us in making generalizable conclusions about the quality of the representations. Hence, the second challenge we focus is the human interpretability problem with these automatically learned representations. For instance, researchers in Twitter analytics are getting interesting results by applying different representation learning models for several valuable tasks such as sentiment analysis, semantic textual similarity computation, microblog retrieval, hashtag identification and so on. In order to understand the core properties encoded in a tweet representation, we evaluate the representations to estimate the extent to which it can model each of those properties such as tweet length, presence of words, hash-tags, mentions, capitalization and so on. This is done with the help of multiple classifiers which take the representation as input. Essentially, each classifier evaluates one of the syntactic or social properties which are arguably salient for a tweet. The result is an application independent, fine-grained analysis of tweet representations generated by different representation learning models. This thesis is one of the initial work to overcome the above-mentioned challenges by proposing novel methods to improve the contexts and interpret the representations. In the growing body of representation learning research, the set of models and framework proposed in this thesis could act as the basic building blocks in the future works attempting to advance the science of building smarter NLP/IR systems.

Abstract

In the era of globalization, vast amount of information is available on World Wide Web. This information is available in various natural languages. In order to access this information, the speakers of different languages have to rely on translation or learn the other language. Learning multiple languages is not feasible for everyone, hence the need of translation arises. In such a scenario, machine translation emerges as an important discipline in the field of computational linguistics that can give access to the vast information within a short time and at low cost. Translating natural languages automatically is one of the most complex and comprehensive applications of computational linguistics [63], and the dream of a perfect fullscale machine translation system still remains an intellectually challenging task. Several approaches, such as rule based, statistical and hybrid, have been tried out to build linguistic tools. All these approaches have their own merits as well as demerits. Pān.inian Grammar, universally admired for its insightful analysis of Sanskrit [74], offers a perspective to language analysis. The main focus of Pān.inian Grammar is to explain how, where and how much information a language encodes that helps in capturing information dynamics in a language [3]. In order to use Pān.inian theory to analyse other languages, it is necessary to model these languages in terms of Pān.inian primitives. We have selected the primary basic notions such as nominal inflections (sup), verbal inflections (tiṅ), basic grammatical constructions (pada), compound constructions (samasta-pada), etc., to analyse the syntax and semantics of natural language sentences. In our research, we have used these concepts to analyse English from Pān.inian perspective and then map English structures into Hindi for handling three major linguistic tasks for English-Hindi machine translation as follows: 1. Finding Out the Meaningful Linguistic Units That Can Participate in a Sentence: This step theoretically accounts for English sentence in terms of Pān.inian primitives such as sup, tiṅ, pada and samasta-pada. Here we find out the syntactic and semantic units called padas and samasta-padas and the grammatical markings that connect these units, the padas and samasta-padas with the other meaningful units in a sentence. Such analysis looks at the words in a sentence as a combination of content words and grammatical markings [125]. Thus, this type of syntactic analysis helps in capturing the flow of information and gives us the shallow parse of the sentences. It is also helpful in various other linguistic tasks such as dependency parsing, word ordering and case marking. 2. Mapping Dependency Parsers’ Output into a Uniform Notation: Parsers play an important role in analyzing the exact meaning of a sentence. The parse tree produced by the parsers formally represents syntactic and semantic information among the words in a sentence. In a machine translation system, various modules like Gender Number Person (GNP) agreement, Word Sense Disambiguation (WSD), reordering of the source language sentence according to target language sentence, etc. are some of the important tasks which require dependency parse as input [89]. Several dependency parsers are available for English viz. Stanford Parser, Link Grammar Parser, CLEAR Dependency Parser, Minipar, Malt and many more [119, 47, 36, 85]. However, no two dependency parse output formats match with each other. These parsers differ on the names of the dependency labels, their representation style and the number of dependency labels [17]. Since the parsers are based on different approaches, and each approach has its own limitations, the parsers produce different degrees of output quality for different syntactic constructions. In order to obtain maximum benefit out of these parsers, one has to combine the strengths of the parsers [24]. Hence arises the need of a common representation schema. Also, the use of computers as an information processing device requires a sophisticated theory for processing the information in a language. Bringing the parsers’ output into one uniform notation provides the system an ability to plug-in any of these parsers without modifying other translation modules, thereby avoiding large amount of manual work that is costly as well as time consuming. 3. Framing Transfer Grammar Rules: Word ordering plays an important role in the translation process between languages. Using the concept of pada and samasta-pada, the source language sentence is manipulated into target language sentence for natural translation. Since the notion of pada overtly identifies the grammatical markers and their respective content words as syntactic units of a sentence, we get a little more freedom for word ordering as an advantage due to the relatively free word order nature of the target language Hindi [23]. The concept of pada also helps in generating direct

Abstract

Healthy heart is a key for healthy life. Unfortunately in many cases the heart function deteriorates over a person’s lifetime, often with tragic consequences. A class of diseases involving heart functionality is termed cardiovascular disease. Among a number of such cardiovascular diseases, cardiac arrhythmia and coronary artery disease (CAD) are two of the most common and potentially life threatening diseases and are the our work is focussed around them. A primary reason for a high casualty figures caused by these diseases can be attributed to the fact that the diagnosis is not done on time. Often, the symptoms are noticeable only at a stage where the person needs immediate medical attention. Accurate diagnosis can only be done at specialized healthcare facilities by trained personnel. The diagnosis is also expensive. This limits its accessibility for major portion of population especially in rural areas. State of the art diagnosis for coronary artery disease typically involves an invasive procedure. Though this procedure is conclusive, it is still painful and risky. Assessing these challenges, there is a need for a widely accessible, inexpensive and preferably non invasive alternative. While looking for alternatives, electrocardiography or ECG has advantages of being accessible, non-invasive, and inexpensive. It is used for diagnosing cardiac arrhythmia, and as a screening test for coronary artery disease. However this screening is inconclusive and other techniques are recommended. Another non invasive technique, phonocardiography or PCG suffers from complex design and sensitive equipment. Which brings us to yet another well known technique, most commonly used for pulse oximetry, the photoplethysmography (PPG). Photoplethysmography can be defined as study of blood volume changes through light. It is non invasive, inexpensive and can easily be made portable thus making an ideal candidate for further investigation. PPG is also very suitable for use in wearable devices technology and such devices are used for ambulatory monitoring. Having all the desired characteristics, PPG is further investigated towards diagnosing cardiac diseases. As PPG as a technique already exists and is widely used, this work is aimed at developing techniques to analyse the PPG data towards possible diagnosis of cardiac diseases. Phtoplethysmography has its own challenges. It has high sensitivity and susceptibility to motion. This is particularly concerning for detecting arrhythmia and coronary artery disease alike as it wrongly affects heart beat detection as well as degrades the natural characteristics of the signal. First contribution of this work is a technique to estimate heart rate in presence of motion induced artifcats using fuzzy entropy of the signal. Once accurate heart rate is estimated, the input signal is tested for possible cardiac arrhythmias such as tachycardia, bradycardia, asystole, ventricular tachycardia and ventricular fibrillation. The fuzzy entropy based method poses some implementation limitations as it is computationally inefficient and requires initial user input. Another approach is developed to overcome these limitations. A quality index is assigned to each pulse. The artifact portion is removed using an appropriate threshold. The remaining clean portion of the signal is used for arrhythmia detection. This technique is faster and more accurate than fuzzy entropy based technique and much more useful for arrhythmia detection. Another important contribution of this work is to explore PPG for detecting coronary artery disease. This involves analysing the morphology of the PPG signal and its second derivative. Characteristic points are obtained from the PPG waveform and its second derivative. Temporal position of these characteristic points is used as a distinguishing feature to classify between healthy subjects and CAD patients. This analysis is carried out using invasive arterial blood pressure (ABP) signal as well. Supervised classification using support vector machine is employed for classifying healthy subjects and CAD patients. It is observed that higher classification accuracy was obtained using PPG signal compared to ABP. Despite certain limitations, we present proof to suggest that PPG can be effective in detecting certain cardiac arrhythmias and coronary artery disease.

Abstract

Change is inevitable and everywhere. Resources, ecosystem, biophysical environment, and land use/cover on the surface of the earth undergo continuous change over time and space. Land cover is the layer of soil and biomass, including natural vegetation, crops and man made infrastructures that cover the land surface. Land use is the purpose for which human exploit land cover [Verburg et al., 2000]. LULC change is probably the most important form of global environment changes due to its direct relevance with human existence and spatio-temporal nature. The land use changes also have significant implications in climatic changes as well [Scavia et al., 2002], which in turn brings the modification in future land use change. There is also a considerable impact of human intervention on ecology and thus land use land cover change, studying these changes over a considerable time frame reveals the complex yet predictable nature of the change. Modelling of these land use changes can prove to be very much useful in order to understand and predict future scenarios. Land use modelling requires analysing the biophysical and socio-economic drivers for land use change and then incorporating these to study the impact of human dimensions on LULC dynamics. The models help us understand the LULC changes for different socio-economic scenarios within its defined biophysical context. This thesis aims at the study, analyse and model the LULC changes taking place in the Godavari river basin in the country of India, and to predict the LULC map of 2025 for the basin. The whole process involved creation of Land Use, demographic, socio-economic, topographic, climatic, infrastructure and biophysical database, Identifying the various change inducing drivers and processes for each class and then modelling the database using both agent based and statistical techniques. The Godavari river basin encompasses an area of 3,13,346 km square across the States of Maharashtra, Andhra Pradesh, Madhya Pradesh, Orrisa, Chhattisgarh and a small part of Karnataka. The major LULC category in the basin is cropland, which forms almost 57% of the geographical area of the basin. Forests occupy about 35% of the basin area and area are located in the central regions in the states of Maharashtra, M.P. and in the eastern parts of Orrisa. Extensive shrub land areas are found in the central and western part of the basin particularly in the undulating and hilly areas and upland areas of rainfed central region. Satellite data of 1985, 1995 and 2005 was analysed to generate LULC maps at 1: 250,000 scale. Two models were then modified for the purpose of analysing and studying the LULC changes: a process based agent simulation model, Agent-LUC [Rajan and Shibasaki, 2001], , and an empirically fitted statistical regression model ILULCDMP [Ashutosh Kumar Jha, 2015]. In Agent-LUC, land cover change agents are land owners, farmers, migrants and/or policy making bodies, all of whom make decisions or take actions that affect land-use patterns and processes. By simulating the individual actions of many diverse actors, and measuring the resulting system behaviour and outcomes over time (e.g., the changes in patterns of land cover), Agent-LUC can be useful for studying the effects in land-use land cover processes. The model is designed and implemented in C++ along with QT libraries for user interface. Whereas, ILULC-DMP, calculates the correlations between the land use changes happening spatio-temporally with the driver datasets and predicts the future land-use change, by applying statistical regressions on those correlations on the current land-use and driver datasets. The model is designed and implemented in stastical modelling language R. The models first predicted the land use map of 2005 from the database of 1985 and 1995, with accuracies of .96 and .88 respectively for Agent-LUC and ILULC-DMP model when validated with the satellite derived land-use data of 2005. Then the models were run on the 2005 data to predict the land-use map for 2025. As per the further runs of the Agent-LUC model, by 2015 there will be an urban expansion of 404 square kilometres and by 2025, around 818 sq. kms of more urban area would be there in Godavari basin as compared to 2005 data. The agricultural expansion will be of 1345 sq. kms and 2752 sq. kms., both expansions will cause the deciduous forests and shrublands to decrease. The ILULC-DMP predicted map of 2025 shows an increase in built up area by 1840 sq. Kms, cropland by 4900 sq kms, decrease in forest and shrub land by 4422 sq. kms and 4290 sq. Kms. And an increase in waterbody area by 1875 sq. Kms.

Abstract

Batteries are electrochemical devices that convert chemical energy into electrical energy. An electrochemical cell of a battery composed of three major components: an anode, a cathode and an electrolyte. The electrolyte is an ionic conductor that provides a medium for transfer of ions between the electrodes of a cell. The amount of energy stored in a battery depends critically on the nature of electrochemical reactions at the electrode-electrolyte interfaces and the choice of the electrolyte. Solid polymer electrolytes (SPEs) consisting of alkali salts dissolved in a solid polymer matrix of poly(ethylene oxide) (PEO) are novel ion transporters that offer several advantages over other conventional electrolytes for use in all-solid-state rechargeable batteries. In order to fully exploit the advantages of SPEs and for rational design of novel SPEs with higher conductivity and higher energy density for future applications, it is necessary to understand the molecular-level details of the mechanism and the critical factors governing the ion conduction in these materials. Direct observation of migration pathways of ions and a quantitative dissection of their en- ergetics in solid polymer electrolytes (SPEs) are essential to understand the molecular origins of barriers limiting the conductivity of these novel materials. Depending upon the interplay between molecular packing and dynamics, SPEs exhibit a wide range of conductivity (10−9 - 10−4 S/cm) at room temperature despite their common polymer matrix. The principal driving forces controlling the degree of conductivity of SPEs including the nature of macromolecular packing, ion-polymer interactions, ion coordination structure, conformational order and mo- bility of polymers are significantly different among the SPEs. However, the precise correla- tions between these molecular factors and the ion transport in SPEs are not well established. It remains unclear whether disordered amorphous or ordered crystalline structures promote conduction of ions in SPEs. The other important challenges that remain in SPE-related re- search are the identification of low-energy ion conduction pathways, characterization of the free energy profiles and the structural and dynamical contributions to the favorable (stable energy wells) and unfavorable (activation energy barriers) ion sites along these conduction pathways. ix Detailed molecular studies are needed to establish a precise correlation between the na- ture of polymer packing, dynamics, energetics, and ion conduction for rational design of SPE-based fast ion conductors. In this regard, molecular dynamics (MD) simulations and enhanced sampling free energy methods play an important role in elucidating the atomistic details of ion transport and the critical ion-ion and ion-polymer interactions in SPEs. In the present thesis, we have employed molecular dynamics (MD) simulation, enhanced sampling methods (for example, the adaptive biasing force (ABF) method, well-tempered metadynam- ics (WTmetaD) simulation, and nudged elastic band (NEB) method) and normal mode analy- sis (NMA) to investigate the structure, dynamics and energetics of crystalline and amorphous phases of poly(ethylene oxide) (PEO) and PEO-based SPEs. Thermal phase behavior of crystalline poly(ethylene oxide) explored using MD simula- tions has shown the possibility of a premelting transition well below the equilibrium melting of the crystal. At temperatures just above the premelting transition temperature, molecules are able to rotate about their long axes, and diffuse along the chain-axis of the crystal. To under- stand the nature of coupling between polymer dynamics and ion transport in SPEs, all-atom molecular dynamics simulations of crystalline PEO3:NaI were carried out and polymer con- formational dynamics, energetics and migration pathways of ions were characterized . The results reveal that in crystalline PEO3:NaI both anions and cations follow helical pathways mirroring the helical symmetry of the polymer. The normal mode analysis (NMA) and the power spectral analysis of MD-derived velocity autocorrelation functions (VACF) have eluci- dated the important roles of backbone twisting of PEO helical chains about their long axes and softening of a few low-frequency collective modes in ion transport in SPEs. The calculated potentials of mean force for cations reveal the existence multiple isoenergetic coordination states separated by low energy barriers in the amorphous SPEs indicating that ion transport is facile and the mechanism of ion conduction is different in the amorphous SPEs than those in the crystalline SPEs.

Abstract

Indoor positioning is gaining momentum for its various applications. As we know that Global Positioning System (GPS) does not work well indoors, while todays more sensitive GPS chips can sometimes get a location x (via receiving signals from enough satellites to determine a location) inside a building. The re- sulting location is typically not accurate enough to be useful. The signals from the satellites are attenuated and scattered by roofs, walls and other objects. Besides, the error range of many GPS chips (tennis court) can be larger than the indoor space itself (small grocery store)! Some indoor positioning solutions work similar to GPS. Many companies tap into Wi-Fi signals that are all around us - including when we are indoors. With a good map of the locations of the access points, a Wi-Fi receiver like a cell phone can be located even indoors. Any application that would depend on indoor positioning may need an exact location. This work on indoor localization in 3D using spherical co-ordinates would have an edge to all the future needs. A combination of Pico, Femto, Wi-Fi, otherwise termed as hybrid localization techniques are used in conjunction with leveling and sectoring. Leveling and sectoring are discussed using base station and Wi-Fi access points and the received signal strength (RSS) nger prints are used to aid in precise localization. Indoor localization applied to physical analytics is also discussed. This thesis work also focuses in an improvement over our above discussed works in terms of achieving more accuracy and reduction of delay in the Wireless Sensor Networks (WSNs). This is achieved by using the Received Channel Power Indi- cator (RCPI) in contrast to RSS. We assume that RCPI shall be used by all chip vendor for all the Wi-Fi devices coming into the market due to its precise way of measuring the Received signal power. This work also focuses on Wireless Sensor Networks (WSN) with respect to the need for optimizing the parallel distributed computational architecture.It also discussed how it can be acheived using our proposed model. This gains impor- tance as identication of an event in WSNs should be done as fast as possible by minimizing the delay. Optimizing the grid based architecture for time complexity, transmission delay and fault tolerance in computing the fusion functions, our work also focuses on localizing an event in an outdoor WSN and respond to the event based on the need as soon as possible. Hence our overall work on 3D localization and wireless sensor networks helps us localize events in both indoor as well as outdoor with reduced time-complexity as well as delay parameters.

Abstract

Many of the real world networks can be represented as graphs. One of the most important problems related to the graphs is to find similar graphs. Due to a large number of properties each graph can possess, it is often difficult to find a similarity measure that captures all the properties of graphs. In this thesis, we discuss a notion of graph similarity based on the composition. The composition of a graph is the set of its important subgraphs and their frequency of occurrence in the original graph. Existing measures for graphs or trees do not capture the idea of the compositions. Our major contributions are directed towards extraction of compositional features of the graphs and trees. To find compositional features we need to find efficient algorithms to enumerate the frequencies of the subgraph templates in the graph. In this work, we present four contributions: (i) Grammars to recognize and enumerate subtree templates, (ii) idea of compositional similarity, (iii) Clique Conversion Coefficient, and (iv) an effective pre-processing step for listing Maximal Cliques. We designed grammars to enumerate the sub-tree templates in ordered trees. These grammars represent a tree using a string of length O(n), where n is the number of nodes in the tree. The frequencies of sub-tree templates are deduced using an algorithm. The results show that with good design of a grammar that can encode the structure of trees and a compatible algorithm assist in efficient enumeration of such compositional features. In this work, we introduce a measure for graphs, Clique Conversion Coefficient(CCC), which captures the clique forming tendency of nodes in a graph. CCC can be used either as a weighted average of a sequence of values or the sequence of values itself. We define the measure with three variants: global, local, and average. CCC is closely related to both Clustering Coefficient and Density of a graph as in they are all designed to find how well the nodes in a graph group together. But as per our experimental results, CCC, in some cases, provided additional information about a graph, which is the measure of the extent to which the nodes in the graph tend to form cliques. Also, we motivate the need for CCC by clustering some real world graphs with CCC as a feature in combination with other features. Our results show that graphs with similar clique forming tendencies are clustered together with the use of CCC as a feature, while without CCC this kind of grouping is not necessarily possible. This kind of Clustering would have many applications in the areas like Social Network Analysis, Protein-Protein Interaction Analysis, etc., where the existence of Cliques plays an important role. In this thesis, we also discussthe clustering of ego networks of large YOUTUBE network using the sequence of values in CCC as features. The quality of these results is verified by contrasting the frequent subgraphs in each cluster obtained. This experiment highlights the utility of CCC in clustering subgraphs of a large graph based on their clique forming tendencies. Also, we designed an algorithm to list all the maximal cliques. Our contribution in this algorithm is the pre-processing step, where we break the graph into biconnected components and prune the nodes having a lower degree and do these two steps iteratively in this order. This method experimentally showed to be very effective for breaking the workload of listing maximal cliques in many sparse graphs.

Abstract

In recent times, online advertising have become the prominent source of revenue for a major part of the Internet economy. Various modes of user interaction like - search engines, social networking, QA systems etc. has evolved forming different channel of advertising like Sponsored Search, Contextual Advertising, Micro-blog Targeting etc. The ad-publishers and the ad-networks need to be watchful about user’s interest when targeting them for their brand or product promotion through these channels. Exploiting user’s interest is of paramount importance while placing the advertisements, as it involves the higher probability of a click on the ad, which offers benefit to all the entities involved. Browsing content, a user is going through, is one of the indication of the things which interests user, which is used by the ad-networks for targeting. The ad relevant to the user’s interest increases the probability of an ad-click, which is beneficial for the entities involved. The field of On line Advertising which deals in placing ads by targeting the content is prominently known as ‘Content-targeted advertising’. The short-text of ads and unavailability of word-distributions on search engines/social-media builds on the vocabulary difference between the user and the publisher/advertiser. This vocabulary gap is one of the major problem while retrieving the relevant ads for the content being targeted. In this research work, our main focus was to bridge the ‘vocabulary-gap’ between the ad-text and browsing-content. Due to vocabulary mis-match we can not retrieve all the ads relevant to the content. In this thesis we have proposed different Semantic Solutions to solve the problem of vocabulary-mismatch between the texts. The two texts (ad and the content to be targeted) in their original forms do not share same vocabulary, so, we can consider them as two different syntactic-spaces. Measuring the relatedness between the two texts with different vocabularies is difficult and not-precise. The solutions proposed involve transforming these two texts to a common semantic space and then measure the similarity between them with higher precision than their actual representation. We proposed solutions for two categories of content-targeted advertising: • Micro-blog Targeting: This is about targeting ads relevant to micro-blogs in social media platforms. • Contextual Advertising: This is about retrieving relevant ads for the web page based on its content. Both categories come with their own challenges, micro-blogs are sparse in nature , involving slangs and inaccurate grammatical sentences with spelling mistakes. Retrieving ads for micro-blogging content is a hard problem. As the micro-blogging content is short and noisy and the ads are short too, there is a high amount of lexical mismatch between the micro-post and the ads. To bridge this lexical mismatch, we have proposed a ‘conceptual approach’ that transforms the content into a conceptual space that represents the latent concepts of the content. The conceptual distribution of the content have been used to measure the relatedness between the micro-blog and the ad. Web-page has a lot of text which needs to be targeted against the ad (very short text), we have proposed a solution which deals with the inherent latent topics of the text. The approach ‘topic models’ transforms the text into the distribution of predefined topics, and then we compare the topical distributions of the content and the ad-text to measure the similarity between them. To build the topic-model, we have used ODP (Open directory project) which has web-pages for numerous categories like, Finance, Automobile, Shopping etc. Our work on the microblog targeting is one of the initial work in the field. In this thesis, we have investigated some of the factors which are helpful while targeting microblogs. We have also compared our proposed approaches with various state-of-the-art techniques like vector-space model and language modeling (syntactic-match), using a taxonomy classification approach for semantic-ad matching, a machine learning approach etc. We empirically show that the proposed semantic models and the ensemble of this models with syntactic models have performed better than all the baselines and also have the substantial and significant improvement in the precision at all the precision levels over the baseline models.