Abstract

Text articles propagating false news have lately become annoying for the Internet users. These articles circulate quickly in the social media platforms and make it difficult for users to bifurcate actuality from fiction. Various social media platforms and instant messaging applications have become the source of these kinds of text articles. Since a large audience is active on these platforms, it becomes easy to propagate media content among the active users. These text articles range from news content belonging to international news to hyperlocal journalism which is propagated for financial or political gains for some organization or community. Users on the Internet easily believe any kind of shared news on such social media platforms. Very few try to verify and then share further these news articles. But many tend to blatantly accept and proceed further to share on various social media and instant messaging platforms because most of the time false news tend to be more interesting with misleading facts. Lately, the scientific community has shown a keen interest in finding better computational methods to segregate such misleading facts from a lot of news articles. Past work on computational credibility analysis has concentrated on fact examination and linguistic features. The primary focus of the credibility analysis task is to learn features which differentiate the false claims from the true claims by finding the conflicting viewpoints. This task of news verification and scoring have been attempted in various ways ranging from traditional NLP methods to machine learning approaches. This work describes a novel architecture and methodology of Credibility Outcome (CREDO) which processes a given text article and gives the trustworthiness score of a textual claim in an open domain setting. CREDO constitutes of various modules which capture different characteristics responsible for the credibility of the textual claim. These characteristics include reliability of the article’s source and writer, semantic comparability between the article and related dependable articles recovered from an information base and the opinion expressed in the textual claim. All these characteristics are learned from various modules and the scores are fed into a neural network architecture which learns the features from these modules pertaining to the overall credibility of the textual claim. The noisy text is also attempted to normalize by using a novel normalizing approach using distributed semantics and ranking approaches to find normalized word during the preprocessing of text, in case the system receive noisy text article as input. Various experiments are conducted on a popular dataset which exhibits that CREDO outruns the state-of-the-art approaches based on linguistic features

Abstract

Micro-blogging sites such as Twitter, Facebook have gained tremendous popularity in the last decade, and have allowed the users to freely write content on the Internet for the purpose of sharing, providing and using the information. They encourage users to express their daily thoughts in real time, which often results in millions of emotional statements being posted online, everyday. This huge amount of user-generated data has resulted in the emergence of a research community that aims to mine social media content and evaluate various linguistic properties associated with the text, such as emotion prediction, irony detection, hate speech detection, gender prediction, sarcasm detection, fake news detection, troll detection etc. However, the writing style of users on microblogging sites tends to be quite colloquial and non-standard, different from the style found in more traditional, edited genre. Authors from multilingual societies tend to write code-mixed posts frequently on social media which results in emotions expressed in text due to the mixture of different natural languages. Code-Mixing (CM) is a natural phenomenon of embedding linguistic units such as phrases, words or morphemes of one language into an utterance of another. The presence of huge amount of code-mixed data on social media has attracted researchers to understand and study the code-mixed texts. While some work has been done on code-mixed social media text and in emotion prediction and irony detection separately, our work is the first attempt which aims at identifying the emotion and detecting the irony associated with Hindi-English code-mixed social media text. In this thesis, we analyze the problem of emotion identification and irony detection in code-mixed content. We present a corpus of Hindi-English code-mixed tweets for Emotion Prediction and Irony Detection. Corpus for Emotion Prediction is annotated with the associated emotion and also the causal language of the expressed emotion. We annotated the corpus for Irony Detection with the labels ironic or non-ironic. For every tweet in the datasets, we annotate the source language of all the words present. Finally, we propose a supervised classification system which uses various machine learning techniques for prediction of emotion and detecting the irony associated with the text using a variety of character level, word level, and lexicon based features. We evaluate our systems on the presented datasets and carry out 10-fold cross-validation.

Abstract

Quantum superposition and entanglement (a special case of superposition) play a pivotal role in achieving information processing tasks which could otherwise be not possible through any classical resource. It is further interesting to note that these very properties constrict us from performing some tasks which are otherwise achievable classically. As quantum entanglement has proven to be a key resource for various information-theoretic tasks, it’s numerous applications makes it imperative to study it’s behaviour. Considerable studies have been done on how to generate entanglement, the chief of which is by using global unitary operations. The thesis further examines the limits imposed on quantum information theory due to the probability structure of mixed states. In mixed states the probability occurs at two levels, first it starts as a description of the system as a statistical mixture, second as the intrinsic superposition that is contained by each entity of the ensemble. We witness how the global unitaries, that can make any pure separable state entangled, fail to generate entanglement for a class of mixed states. The discussion begins with the definition of separable states and non-local states. It is then followed by a concise study on the absoluteness of the above mentioned class of states. Following which is an extensive study on absoluteness of non-negative conditional von Neumann entropy, which happens to be the decisive part of the second chapter. The thesis concludes by discussing another limit that superposition imposes on quantum theory. The impossibility to clone coherence of an arbitrary state. It also addresses the logical implication of no-cloning of coherence which is the famous no-cloning theorem.

Abstract

Developmental dyslexia, considered genetic in origin, is associated with unexpected difficulties in reading, writing, and comprehension, despite normal intelligence and socioeconomic status. Its nature and prevalence rate differ across languages. These differences gave rise to many debates about its universality and specificity. Additionally, prevalence estimate of dyslexia in different countries seems to be related to the shallowness of the orthography. Telugu is an alpha-syllabic/ Akshara-based language with a one-to-one mapping between the grapheme and its constituent phoneme. Moreover, on the granularity-transparency scale, Telugu orthography falls on the coarse-transparent level. In Telugu, phoneme is not the smallest grain size nor the reading instructions phoneme based as in English. In that case one can ask how a phoneme deficit could underlie reading difficulties in Telugu. Thus we hypothesized that reading difficulties of Telugu native speakers would be perceptual in nature rather than phonological. This thesis aims to address the reading difficulties of Telugu native speakers, specifically perceptual and phonological deficits. Further, appropriate intervention methods are devised in this thesis to address specific deficits. We conducted a set of perceptual and phonological tests on age-matched groups (dyslexic and non-dyslexic) and observed significant group differences on perceptual tests but not on the phonological tests. The results are in line with those obtained on other transparent languages. We propose a bilingual reading model for Telugu and other Akshara languages. The qualitative analysis of the reading errors indicates that there is impairment of the visual/letter detector components that lead to peripheral dyslexia and at the sub-lexical route causing the vowel-letter dyslexia among Telugu native speakers. These errors are also related to visual and auditory attention deficits. Consequently, we designed interventions that could assist in improving visual and auditory skills. The contributions of the thesis could provide the foundation for devising more accurate diagnostic protocols and offer effective interventions for children with reading difficulties.

Abstract

Human brain is undoubtedly the most magnificent yet delicate arrangement of tissues. This serves as the seat of such a wide span of cognitive functions and behaviors. The neuronal activities within every neuron, collectively observed over network(s) of these interconnected neurons, manifest themselves into patterns at multiple scales of observations. Many brain imaging techniques such as fMRI, EEG, MEG etc. measure these patterns as electro-magnetic responses. These patterns supposedly play the role of unique neuronal signatures of the vast repertoire of cognitive functions. Experimentally, it is observed that different neuronal populations participate coherently to generate a signature for a cognitive function. These signatures could be investigated at the micro-scale corresponding to responses of individual neurons to external-current stimuli, at the meso-scale related to populations of neurons that show similar metabolic activities and in turn these populations, also known as regions of interest (ROIs), communicate via complex arrangement of anatomical fiber pathways leading to signatures at the macro-scale. The holy grail of neuroscience is thus to computationally decipher the interplay of this complex anatomical network and the complex functional patterns corresponding to the cognitive behaviors at various scales/levels. Each scale of observation, depending on the instruments of measurement, has its own rich spatiotemporal dynamics that interacts with higher and lower levels in complex ways. Large-scale anatomical fiber pathways are represented in a matrix that accounts for inter-population fiber strength known as structural connectivity (SC) matrix. One of the popular modalities to capture large-scale functional dynamics is resting-state fMRI, and statistical dependence between these inter-population BOLD signals is captured in functional connectivity (FC) matrix. There are many models that provide computational accounts for the relationship between these two matrices as deciphering this relationship will provide the mechanism by which cognitive functions arise over the structure. On one hand, there are many non-linear dynamical models that describe the biological phenomenon well but are expensive and intractable. On the other hand there are linear models that compromise on the biological richness but are analytically feasible. This thesis is concerned with the analysis of the temporal dynamics of observed resting-state fMRI signals over the large-scale human cortex. We provide a model that has a bio-physical explanation as well as an analytical expression for FC given SC. Reaction-diffusion systems provide a computational framework for the emergence of excitatory-inhibitory activities at the populations as reactions and their interactions as diffusion over space and time. The spatio-temporal dynamics of the BOLD signal governed by this framework is constrained with respect to the anatomical connections thereby separating the spatial and temporal dynamics. Covariancematrix of this signal is estimated thus getting an estimate of the functional connectivity matrix. The covariance matrix or the BOLD signal in general is expressed in terms of the graph-diffusion-kernels thus forming an analytically elegant expression. Most importantly, the model for FC abstracts out biological details and works in the realm of spectral graph theoretic constructs providing the necessary ease for computational analysis. As this model learns the combination parameters of multiple diffusion kernels and kernels themselves, it is called Multiple Kernel Learning (MKL) model. Apart from superior quantitative performance, the model parameters may act as biomarkers for various cognitive studies. Albeit, the model parameters are learned for a cohort, the model preserves subject-specificity. These parameters can be used as a measure for inter-group differences and dissimilarity identification as has been employed for age-group identification as an example in this thesis. Essentially MKL model partitions FC into two constituents: influence of the underlying anatomical structure into diffusion kernels and the cognitive theme of temporal structure into the model parameters, thus predicting FCs specific to subjects within the cognitive conditions of the cohort. Even though MKL is a cohort based model, it maintains sensitivity towards anatomy. Performance of the model drastically drops down with alterations in SC and model parameters, but does not overfit to the cohort. Resting state fMRI BOLD signals have been observed to show non-stationary dynamics. Such multiple spatio-temporal patterns, represented as dynamic FC matrices, are observed to be cyclically repeating in time motivating use of a generic clustering scheme to identify latent states of dynamics. We propose a novel solution that learns parameters specific to the dynamic states using a graph-theoretic model (temporal-Multiple Kernel Learning, tMKL) and finally p

Abstract

Synthetic or artificial speech has been developed steadily during the last decades. Especially, the intelligibility has reached an adequate level for most applications, especially for communication impaired people. This thesis is concerned with the development of deployable text to speech (TTS) systems for Indian languages. The research described here was conducted with two principle aims: To build stable, scalable and unrestricted synthesis systems for Indian languages; and to design such systems by (a) making observations about the structure of the data and (b) keeping the human in loop so that we end up with systems both acceptable and efficient. In order to do that, I first present a baseline synthesis system for Indian languages built employing unit selection and concatenation as the algorithm and syllables as the basic unit with reduced vowel epenthesis based backoff to handle the missing units. During synthesis, the required utterance specified as a string of syllables help in the selection of appropriate units from the stored database. The system uses word to phone mapping to handle the words from a different language (English) and the selected units are concatenated using a variant of Waveform Similarity Overlap Addition (WSOLA) based method. Two extensions to the system are discussed. • Inspired by the smoothness of the speech obtained after the backend concatenation algorithm, a method is presented to exploit the signal correlation as a join cost in the viterbi algorithm itself with an aim to further improve the naturalness of the system. Two variants of signal similarity are examined - cross correlation based and average magnitude difference function based. • Modeling the prosodic characteristics is an important trait for a text to speech system. Deep generative models which are the standard today benefit from a low dimensional continuous input rather than an array of 1-hot k encoding for the phonemes as is used today. A method has been discussed to obtain continuous representation for the phonemes using a matrix factorization approach and show that the representation can be used to model prosody, by predicting the phoneme durations. The system can be retrained in 6 hours on the same languages and should take approximately the same amount of time for any new language.

Abstract

Abstract International Relations deals with the interaction of political entities in the international arena. Iden- tifying and analyzing these interactions in near real-time is important to predict upcoming political alignments and fathom upcoming conflicts. This effort is widespread as part of governmental diplo- macy. Our approach presents a computational notion of this same phenomenon. This requires analysis and data which was scarce and highly secured. Technology and computation, was limited and expen- sive. Classified documents regarding the interactions weren’t public. With increased digitization and penetration of internet across domains, this research has renewed impetus. One of the major tasks of analyzing actor interactions is to identify events involving actors from the news. Event is one of the most important temporal phenomena. It is that which has a beginning and an end. Automatic coding or classification of events happening in international relations is an important part of social science data ecosystem. The last few decades have witnessed significant work in detecting political events in the international arena. Most of the current work involves classification of news based on pattern matching from a large set of verb patterns, political actors, compound nouns, compound verb phrases, reference to pronouns and deep parsing of sentences in news articles. We introduce a methodology for extracting international relations events using news media data. This method involves a graph based unsupervised learning method for extracting topics in the news articles and identifying events based on various entities: actors and other spatial and temporal features. Since the same event might be reported by multiple news media across the world, multiple events corresponding to the same real-world event would be detected. This is referred as Event Coreference in NLP. We work on the problem of Event Coreference resolution which consists of finding clusters of event mentions that refer to the same event. Since events are complex, they co-refer fully as well as partially. We extract full co-reference, partial co-reference (sub-event and member-of relations) for the events extracted based on rule-based approach and obtain substantially good results. International relations events require an ontology for the classification of events to analyze the in- teractions among actors. We propose a novel mediation ontology for the categorization of international relations events and also introduce a novel method of classifying events through the mediation ontol- ogy using Beth Levin Verbs Classification, word2vec and Universal dependencies. The selected feature space is a result of mapping language entities to ontological entities. Hereafter, we explore various applications of international relations events. We demonstrate the relevance of our work in IREvent2Story. Event detection is a key aspect of story development which is itself composed of multiple narrative layers. Most of the narratives are template-based and follow a narration theory. We demonstrate a narrative from events detected in the international relations which presents interactions of international actors over various topics and other interactive visualizations. Another application we demonstrate is IREvent2Perspectives. Media is a classic example of the freedom to expression which may incorporate polar sentiments on the reportage. We create a system for readers with various perspectives on the same international relations event from various news sources across the world. Our study opens newer avenues for journalists and policy makers to exploit the opportunities for data driven diplomacy. It is hoped, that this research finds base especially with national and international organizations that the inexact and amorphous nature of world diplomacy is handled on hard facts and real analysis.

Abstract

Last few years have seen a tremendous increase in interest for autonomous driving. As advances have been made in this field, autonomous driving has faced various challenges. In this thesis, we present vision based solutions to curb and intersection detection problems faced by self-driving cars. In the first part of the thesis, we present a discriminative approach to the problem of curb detection under diverse road conditions. Curb detection is a critical component of driver assistance and autonomous driving systems. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixel-wise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further, we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over a large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset. While it is important to detect road boundaries, another interesting problem is to know how the boundaries fork and lead to an intersection. In the second part of the thesis, we focus on the problem of road intersection detection. Long-short term memory networks(LSTM) models have shown considerable performance on a variety of problems dealing with sequential data. In this thesis, we propose a variant of Long-Term Recurrent Convolutional Network(LRCN) to detect road intersection. We call this network as IntersectNet. We pose road intersection detection as binary classification task over a sequence of frames. The model combines deep hierarchical visual feature extractor with recurrent sequence model. The model is end to end trainable with a capability of capturing the temporal dynamics of the system. We exploit this capability to identify road intersection in a sequence of temporally consistent images. The model has been rigorously trained and tested on various different datasets. We think that our findings could be useful to model behavior of autonomous agent in the real world.

Abstract

The thesis brings out technology impact on the two key political processes viz. news reporting and public protests. In the first part theoretical models are developed for the attention seeking ability of the news headlines online, based on the theories of human curiosity termed as the information seeking behaviour without utilitarian motive. A classifier is built to identify the clickbaity headlines indicating high degree of curiosity they generated in reader’s mind. The methodology is also used to study the headline dataset scraped and collated from the digital newspaper start-ups in India for their ability to attract reader views and attention. News headline gets spotted by the reader for the novelty of the topic, it creates surprise in readers’ mind by the choice of word arrangements used. Headline stoking optimal reader anticipation attracts a click pointing to its autonomous significance independent of the narrative. Finally headline’s structural, syntactic and semantic features create information gap in reader’s mind, by elevating the information reference point and the headline which is ”self-important”, salient, and promises to close the information gap piques reader curiosity and triggers clicking behaviour. The work also computationally validates a century old postulate about the relationship between curiosity and stimulus levels by computing novelty and surprise from Facebook reader response to news headlines. We have plotted the curious responses of reactions, shares and comments against the stimuli causing novelty and surprise to check their impact on the reader curiosity. The result of inverted ”U” curve, is as per the psychological modelling done during late 19th century by Wilhelm Wundt hypothesizing intermediate arousal potential. The second part of the thesis brings out the biography of public outrage online by computationally analyzing two recent incidents viz. Jyoti Singh incident(2012) at Delhi and murder of noted journalist Gauri Lankesh(2017). This study has tried to prise-open the manner in which online outrage builds up and evolves into politically meaningful public conversations. It suggests that outrage by itself has a short life, is intense in its expression but dies quickly. However, it appears that where it builds on a certain robust mapping of emotions and incidents to each other, it has a good chance to develop an afterlife as a larger public conversation, and thus impacts politics.

Abstract

Inverse Kinematics (IK) problem deals with finding an appropriate actuator configuration that makes the end effector Cartesian coordinates and pose match the given coordinates and pose. In most of the cases, it is enough if we can solve IK for the given coordinates whatever may be the pose. Real time calculation of inverse kinematics with dynamically stable configuration is of high necessity in humanoid robots as they are highly susceptible to lose balance. General Inverse Kinematic solvers may not guarantee real-time control of the end-effectors in external coordinates along with maintaining stability. This work proposes a methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using Deep Reinforcement Learning (RL). Our approach is based on the idea of exploring the entire configuration space of the robot and learning the best possible solutions using an actor-critic based policy learning in continuous action spaces, Deep Deterministic Policy Gradient (DDPG). The proposed strategy was evaluated on the highly articulated upper body of a humanoid model with 27 degree of freedom (DoF). The trained model was able to solve inverse kinematics for the end effectors with 90% accuracy while maintaining the balance in double support phase. Following the success of learning a general IK solver for goal reachability tasks of a single hand of humanoid, a more challenging problem of solving IK for both hands of the humanoid simultaneously is taken up. For addressing this problem, the proposed methodology was extended with significant changes in the state vector and reward function modelling. The extended strategy was then evaluated on the highly articulated upper body of the given humanoid for learning multi-goal reachability tasks of both hands along with maintaining stability in double support phase. Results show that the trained model was able to solve inverse kinematics for both the hands, where the articulated torso contributed to both the tasks. However it was observed that DDPG was unstable and in some cases even though it was stable initially, the cumulative reward started degrading later. Consequently, this works moves on to address these instability issues by using multi-task reinforcement learning. However, most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where actions between different tasks are not fully separable and share a set of actions. In case of tasks where actions are fully separable, each task policy can be learnt independently using different policy networks. However when the actions are not fully separable, the policies cannot be learnt independently. In such environments a compound policy may be learnt with shared neural network parameters, which performs multiple tasks concurrently. However such compound policy may get biased towards a task or the gradients from different tasks negate each other, making the learning unstable and sometimes less data efficient. In this work, we propose a new approach for simultaneous learning of multiple tasks sharing a set of common actions in continuous action spaces, which we call as DiGrad (Differential Policy Gradient). The proposed framework is based on differential policy gradients and can accommodate multi-task learning in a single actor-critic network. We also propose a simple heuristic in the differential policy gradient update in case of partially separable actions to further improve learning. In order to show the efficiency of framework, the proposed architecture was tested on the given humanoid for learning multi-goal reachability tasks (each end effector has a different goal) and the results were compared with that of DDPG. Training results show that DiGrad converges faster than DDPG and removes the instability in training. Finally a comparative analysis with different settings in DiGrad along with DDPG was shown.