Abstract

This thesis adds to the knowledge of a chemical named entity detection and chemical role labeling. We present guidelines for chemical named entity detection in the Examples section of patents. In the process, we created the second-largest chemical NER dataset, the WEAVE corpus. We also developed methods for computational named entity recognition. We describe the models along with their features and gaps. We simultaneously extend the WEAVE corpus with chemical role labels in the Examples section of patents. Chemical role labels tend to change temporally as the reaction proceeds step by step. A chemical that has the product’s role label in the first step would have a reactant role label in the next step. This fluid nature of labeling the same chemical in a coherent reaction discourse multiple times is yet to be computationally modeled. The dataset developed in this thesis would be the first step in that direction. We evaluated the baseline performance of the WEAVE corpus using Deep Affix-base BiLSTM-CRF based neural architecture. The model achieves an F-score of 91.37%, near state-of-the-art result. We also compare the baseline performance with BioCreative-V CHEMDNER-patents corpus. It was found to be complementary in nature, hence could be used for transfer learning. In future work, we suggest how this corpus could build systems that could automatically select the chemical reaction from literature and infer the reaction rule for a noninteractive organic synthesis design system.

Abstract

Music is omnipresent and has existed since time immemorial. In general, individuals’ response to music, be it in terms of movements or listening strategies, tend to be dictated by features intrinsic to music such as rhythmic structure, genre, in addition to extrinsic factors such as mood states, socio-cultural norms, amongst others. However, at the same time, human differences in physical, psychological, and behavioural traits affect the decisions we make and the experiences we pursue in daily life. Individual differences can refer to gender, cognitive styles of thinking, personality, amongst others. In this thesis, we try to expand our understanding of how individual characteristics modulate our musical behaviour. The study and analyses presents various angles of approaching musical behaviour in an interdisciplinary manner, particularly in the areas of embodied music cognition and music consumption. We particularly investigate musical behaviour in two broader sub-domains: Active Music Engagement, which entails corporeal involvement and Passive Music Engagement, which involves listening to music. In the first part, we look at decoding individual differences via active music engagement. The paradoxical balance between universality and individuality in human motoric responsiveness to music makes it interesting to note that all the individuals’ traits are encoded in free dance movement. The study addresses this by identifying individual differences, specifically gender, cognitive styles, Big Five personality traits, and music preferences using free dance movements via our proposed Machine Learning model. We further demonstrate the robustness of the proposed model by testing its efficacy on another data set and further providing conclusive evidence about the learned models’ generalizability. The results of the study support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music. In the second study, which deals with passive engagement, we investigate out how individual differences, specifically personality, are associated with the kind of emotional experiences one seeks from lyrics. We chose to investigate lyrics specifically because they are under-explored in the field of music information retrieval despite them playing a crucial role in eliciting emotions. Firstly, we sought to identify emotional connotations of music based on lyrics following which we associate them with individual differences represented by personality traits. To this end, first we propose a novel deep learning architecture to identify emotional connotations of music based on lyrics, which we further compare with the existing deep learning and even traditional methods on relevant datasets and show state-of-the-art performance. Subsequently, we use this model to extract emotional preferences of individuals mined via online music streaming platforms and associate them with inherent personality traits. Our findings validate our theory that various types of emotions conveyed by songs have a unique relationship with individual traits. This study contributes to a better understanding of the relationship between broad personality dimensions and the emotional experiences one naturally seeks on online music streaming platforms. Both of our studies corroborate previous research in the field in addition to providing novel findings in both the aspect of musical behaviour being modulated by individual differences.

Abstract

Next generation wireless networks (5G and beyond) present the challenges of serving clients via channels which are not traditional point-to-point communication channels. The study of efficient high throughput communication techniques for broadcast channels, interference channels, multiple access channels, device-to-device (D2D) communication, all obtain relevance in the present wireless communication scenario. The technique of utilizing local storage (which has become quite affordable, thanks to advances in hardware design), either on the client’s device or in a nearby location, for aiding communication services on a broadcast channel was introduced formally in the landmark paper by Maddah-Ali and Niesen, under the title of Coded Caching. In this paper, it was shown that a combination of (a) carefully utilizing the local storage or cache available to individual clients, and (b) coded transmissions during the delivery phase, brings tremendous gains in the rate of delivery of information of a broadcast channel. To obtain this large gain in the rate, the coded caching schemes in the landmark paper and state of the art literature, require that the files at the server be divisible into a large number of parts (this number is called subpacketization). In fact, most schemes require the subpacketization to be growing asymptotically as exponential in some r th root of the number of users K, with r being some positive integer. On the other extreme, few schemes having subpacketization linear in K are known; however they require extremely large number of users to exist, or they offer only little gain in rate. Because of these reasons the state of the art coded caching schemes are impractical for applications, even though they are theoretically sound. The first objective of this thesis is to propose a new coded caching scheme with low subpacketization and moderate rate gains utilizing projective geometries over finite fields. The proposed scheme achieve the asymptotic subpacketization, which is subexponential in K or exponential in O((log K) 2 ) (thus improving on the root-of-K exponent) and has a small cache requirement. As a special case of our proposed scheme, we get a new linear subpacketization scheme, which has a more flexible range of parameters than existing linear subpacketization schemes. Extending our techniques, we also obtain low subpacketization schemes for other multi-receiver settings such as D2D communications and distributed computing. We show numerical comparisons of our scheme with existing popular and state-of-the-art schemes, and see that our schemes achieve lower and practically relevant levels of subpacketization in many situations at the cost of higher rate (or equivalently, a lesser caching gain). In another line of work, secretive coded caching was proposed in the literature, where the cache content and the transmissions are required to be such that each user can decode only the requested file by that user, and no information about other files. In non-secretive coded caching, it was shown in the literature that commonality among the user demands help in reducing the rate of communication. The direct adaptation of this result to secretive coded caching is not possible because of the presence of an unique key in each transmission. The second objective of this thesis is to propose an improved (reduced rate) perfect secretive coded caching scheme by exploiting commonality among the user demands. The issues addressed in this thesis are summarized as follows: • Based on the ideas from Projective geometries over finite fields, this thesis leverages the set containment and the subspace containment properties, to develop subexponential and linear subpacketization coded caching schemes for practical number of users and low cache sizes. • This thesis extends the low subpacketization coded caching scheme developed (in this thesis) for the broadcast channel to – Coded caching in D2D networks. – Distributed computing framework. vii • By studying the precise leakage properties of a modified scheme of a state of the art secretive coded caching scheme, this thesis develops an improved (reduced rate) secretive coded caching scheme by exploiting commonality of the user demands

Abstract

In this work, we present a decentralized collision avoidance and motion planning algorithm for multirobot payload transport systems (PTS) navigating through dynamic environments that has applications in various industries, assembly lines, and warehouses for transporting several payloads simultaneously by multiple PTS from one place to another. A PTS is a formation of loosely coupled non-holonomic robots that cooperatively transport a rigid or deformable payload. Each PTS has one leader and multiple followers where the followers maintain a desired orientation with respect to the leader. Each PTS navigates through an environment having several other PTS and static obstacles. The objective of each PTS is to (a) Remain in formation all the time, (b) Reach the desired destination in minimum time and with minimum deviation from the desired path, (c) Avoid collisions with other PTS and static obstacles and, (d) Navigate through narrow spaces by respecting deformability constraints of the payload. The terms PTS and formation are used interchangeably in this thesis. Real-time collision avoidance for such systems is challenging due to the deformability of formations, high dimensional multi-robot non-convex workspaces, and heterogeneity in the type of PTS. We resolve the above challenges by embedding workspaces defined by a multi-robot collision avoidance algorithm and multi-scale repulsive potential fields as constraints within a decentralized convex optimization problem. We initially address the problem for rigid formations where we assume the payloads being carried to be rigid. We propose the Formation-ORCA (FORCA) algorithm for decentralized collision avoidance and motion planning of rigid payload transport systems. FORCA is a combination of optimal reciprocal collision avoidance (ORCA) and decentralized leader-follower formation control algorithms. We later address the problem for deformable formations where the formations can be deformed based on the deformability limits of the payload being carried. We present the PotentialField-Formation-ORCA-MPC (PF-ORCA-MPC) for decentralized collision avoidance and motion planning of deformable payload transport systems and has primarily two main steps to plan the motion of each formation. First, we compute collision-free multiscale convex workspaces over a planning horizon using a combination of ORCA and repulsive potential fields. Subsequently, we compute the motion plans of formation over a horizon by proposing a novel formulation for collision avoidance, and we leverage a model predictive controller (MPC) to solve the problem. FORCA and PF-FORCA-MPC algorithms are validated through extensive simulations varying in i) number of formations, ii) configuration of formations, iii) number of static obstacles, iv) density and complexity of environments. Through a narrow corridor simulation, we show the ability of formations to deform themselves in order to squeeze in through tight spaces. Through an antipodal simulation, we show that there is roughly 10% decrease in time taken for the formations to reach their destinations when deformability could be exploited using PF-FORCA-MPC. Additionally, we show that we attain a low net average execution time of 0.025(s) per iteration, even in high-density cases with around 60 robots in the vicinity. The results validate that our solution facilitates real-time navigation of multiple formations allowing the formations to successfully reach their destinations while remaining in formation and also avoiding collisions. The proposed approach applies to holonomic robots as well, provided a holonomic version of formation controller is used. The algorithm does not make any assumptions regarding the size and number of robots in the formation. Hence, it can be incorporated in any general payload transport system of arbitrary size and configuration, as shown in the simulations. Being a decentralized method, our algorithms computationally scale well with an increase in the number of robots and formations used. We additionally conduct experiments in the gazebo simulator and present a proof of concept using real robots.

Abstract

“We, the people” is the most popular phrase from constitutions. Constitutional preambles play a significant role in establishing national identity. In spite of the fact that the number of countries including preamble as part of their constitutions has been on a rise, preambles have received scant attention in academia. Most constitutional studies either completely leave out preambles or study an individual preamble in isolated space. Also, the comparative studies of the preambles are difficult to achieve because of the diverse backgrounds nations emerge from. This thesis focuses on studying preambles across the world under a single umbrella. We propose a meta-structure for preambles to facilitate their studies that encompass spatial and temporal differences. The proposed meta-structure is built on the foundations of the elements constituting a nation-state as well as the active preambles of the world. As part of the meta-structure, we present seven categories - ‘People’, ‘State’, ‘Obligations’, ‘Aspirations’, ‘Principles’, ‘Source of Sovereignty’ and ‘Constitution Adoption Event’. One of the most important functions of a preamble is to establish the collective identity of a nation. Nations establish a politically functional identity of people prior to detailing the clauses of state functioning as part of their constitutions. We dwell deeper into the ’People’ category, which refers to the identity elements in preambles. This study presents various categories which are instrumental in establishing collective identity in preambles. Further, we study the thread connecting preamble, conflict, and identity. Our study shows that the way preambles imagine and narrate identity can turn preambles into instruments of national consolidation or arenas of conflict. Finally, we show that the meta-structure can be used to understand the impact of the preamble on external aspects. We ride on the proposed meta-structure to study the correlation between the progress of a nation and its preamble. We present a statistical study between various indicators that reflect the growth of a nation and the presence or absence of various elements in preambles. We use the study to present an analysis of the impact of meta-categories of preambles on various growth indicators in economic, social, and infrastructural domains.

Abstract

Sequence learning plays a central role in the acquisition of many daily life motor skills such as typing or playing the piano. Several canonical experimental paradigms such as the serial reaction time task, discrete sequence production task and m × n task have been proposed to study the typical behavioral phenomenon in sequencing tasks. Such paradigms are externally-specified, where the environment or the task paradigm extrinsically provides the sequence of stimuli that guides the motor actions. Such paradigms differ from a class of more realistic motor tasks that are internally-guided, where the sequence of motor actions is self-generated or internally-specified. Most previous studies on discrete sequencing have employed externally-specified paradigms and therefore, the cognitive mechanisms underlying skill learning in internally-guided sequencing paradigms remain largely unexplored. This thesis presents an empirical and computational investigation of skill learning in internally- guided sequencing. We employ the Grid-Sailing Task (GST) as a canonical paradigm to study internally- guided sequence learning. The GST requires navigating by executing sequential keypresses, a n × n grid from start to goal (SG) position while using a particular key-mapping (KM) among the three cursor- movement directions and the three keyboard buttons. In the first study, we investigate the learning processes involved in internally-guided sequencing. The participants performed two behavioral experiments – Single-SG and Mixed-SG condition. The partici- pants first completed the Single-SG condition, which required performing GST on a single SG position repeatedly. By showing performance-related improvements in various behavioral measures such as the execution time and reward score, we show that motor learning contributes to the trajectory-specific learning in GST with the repeated execution of the same keypress sequences. The Mixed-SG condition involved performing GST using the same KM (from Single-SG condition) on two novel SG positions presented in a random, inter-mixed manner. Since the participants utilize the previously learned KM, we anticipate a transfer of learning from the Single-SG condition. The acquisition and transfer of a KM-specific internal model facilitate efficient trajectory planning on novel SG conditions. The acquisi- tion of such a KM-specific internal model amounts to trajectory-independent cognitive learning in GST. We provide evidence for the role of cognitive learning in GST by showing transfer-related performance improvements in the Mixed-SG condition. In a subsequent study, we probe the involvement of a particular motor learning process called motor chunking. Motor chunking is a phenomenon which enables efficient execution of the motor sequences by chaining several elementary actions into sub-sequences called motor chunks. The participants per-formed GST on a 10 × 10 grid, executing the same trajectory repeatedly throughout the experiment. We provide empirical evidence for motor chunking by showing the emergence of subject-specific, unique temporal patterns in response times. Our findings show spontaneous chunking without pre-specified or externally guided structures while replicating the earlier results with a less constrained, internally guided sequencing paradigm. In another study, we employ an inter-manual transfer task to examine the stage-wise transitions in motor sequence learning. The participants performed GST on inter-leaved normal and transfer blocks. The dominant hand was used on the normal block and the non-dominant hand was used on the transfer block. The length of the first normal block varied across days. We found increasing differences in execution time between the normal and transfer blocks across days as the effector-dependent learning consolidated. Our findings confirm a switch from the effector-independent cognitive learning phase to the effector-dependent motor learning phase after substantial practice. We then situate internally-guided sequencing in a dual-process account of skill learning and propose computational analogues for the goal-directed and the habitual controller. We propose two hybrid rein- forcement learning frameworks that integrate model-based and model-free mechanisms to account for the dual learning processes. Using simulations and model-fitting experiments, we compare the proposed hybrid frameworks, namely, value-of-information based arbitration and weighted-hybrid arbitration. We show that weighted-hybrid arbitration describes the empirical data better than other models. Our pro- posed framework gives a computational account of the learning in internally-guided sequencing.

Abstract

The structure of vegetation changes rapidly due to various reasons influenced by both anthropogenic and natural factors, potentially resulting in degradation and deforestation. Understanding and analyzing the changes in vegetation cover is very important in several aspects including climatic changes, water budget, ecological balance and especially to undertake necessary conservation measures. Spatiotemporal mapping and monitoring of vegetation using remote sensing satellite data help in assessing the health of vegetation and imply better management practices to safeguard them. Further, the development of vegetation indices using remote sensing satellite data provides better insights about the growth patterns, seasonal changes and health conditions of the vegetation. Normalized Difference Vegetation Index (NDVI) is one of the most widely acknowledged indices for vegetation related studies. The generalized annual NDVI profile for vegetation increases with the plant growth and reaches a peak or plateau. Later the profile falls off eventually with plant death or leaf senescence / seasonal changes. Thus, the NDVI series provides a means to describe plant phenology. To interpret changes in vegetation along with the seasonal and inter-annual changes, time series of NDVI data is used in the current study. The concept of neural network has gained much significance in the analysis of vegetation dynamics using remote sensing satellite data. After exploring various approaches available for prediction of vegetation dynamics in the literature and their drawbacks this study has proposed the application of Long-short term memory (LSTM) network, a variant of Recurrent Neural Network using MODIS NDVI time series data sets to assess vegetation dynamics. In the current study various experiments were carried out to demonstrate the potentiality of LSTM network in predicting vegetation dynamics. The first experiment includes a dataset of 861 7-day composite MODIS NDVI images from January 2000 to June 2016 for making the time series. The data is segregated into three sets as the training set (70%), validation set (20%), and testing set (10%). To check the reliability of the experiment two different regions were selected after extensive research in the current investigation. These include different terrains in the Great Nicobar Island, one region along the coast where vegetation has severe ecological damage due to the 2004 Indian Ocean tsunami and the other, an interior region that remained imperturbable during the tsunami. LSTM network is trained with the NDVI values for both the regions separately to predict the future vegetation dynamics. To measure the accuracy of the LSTM network, root mean square error is calculated. The resulting plots for both the experiments indicate that the LSTM neural network follows the series in addition to coinciding with the required time series. Also, an unanticipated change in the trend of the NDVI series was well adapted by the network and was able to predict future NDVI values with good accuracy maintaining RMSE less than 0.03 without providing any supplementary data. To further investigate the robustness of the proposed prediction technique to variations in data composite periods, noise and the study area two additional experiments are executed. The results obtained by using 16-day composite MOD13Q1 datasets containing 455 images of Great Nicobar Island suggested that the composite period has no effect on the performance of LSTM network when there is fairly enough data to train the network. To analyze the influence of noise in the time series on LSTM network, three noise reduction techniques (Fast Fourier Transform, Mean Value Iteration and Whittaker smoother) which have different noise reduction levels are selected. The results have indicated that FFT based approach had the least error rate as it is best at smoothing the time series of all the three methods and MVI had high error rates. Although FFT had least error rate, the original time series was not preserved well by the approach and the series was over smoothed. MVI performance was relative to the noise levels in the original series as this approach is based on the mean of adjacent values. Whittaker smoother on the other hand was able to smooth the series reasonably well and preserve true values. The RMSE values were observed to be around 0.02 for this approach in general. It is further understood that LSTM is independent of the properties of the study area by conducting the above experiments in Uttarakhand region that has different climatic, vegetation and topological conditions from. Further, to comprehend if a complex model like LSTM is required for vegetation studies and whether LSTM performs better than the vegetation predictions techniques in practice, all the above experiments are computed with two popularly used prediction techniques Artificial Neural Networks (ANN) and Support Vector Regr

Abstract

The evolution of dating technologies from matchmaking only services to dating technology ‘mediums’ has brought tremendous shifts to both dating paradigms as well as dating cultures over the years. Personal ads encouraged the shift in the agency to choose partners from family to individuals and computer dating in the 60s and 70s played a hand in challenging traditional norms of limiting oneself to one partner and only from within one’s social class. These dating technologies, however, only served as matchmaking services leaving all the actual preliminary ‘dating’ or ‘gauging’ to in-person, FtF (face to face) meetings. The launch of online dating through Match.com transformed dating experiences by bringing both matchmaking and preliminary dating under one umbrella, enabling finding and gauging matches as potential partners through chat on the same medium. From newspaper ads to VHS tapes, Match.com, and now Tinder, the aim of dating technologies has remained the same - to gauge potential dates that could spark a good real-life date. In the meanwhile, fully immersive VR has evolved as a powerful, embedded, and immersive technology medium to transform dating experiences once again. While existing chat-based dating mediums like Tinder claimed the benefits of internet technologies going mobile, fully immersive VR displaces mobility in favour of bodily immersion. Each dating technology provides different mechanisms to ‘gauge’ matches depending on its affordances with online dating web and mobile apps already thoroughly explored by existing literature. However, there is no rigorous research examining ‘dating’ or romantic interaction in VR, despite social interaction being a serious topic of exploration in the HCI community. This thesis aims to push the discourse on social interaction a step further by analyzing the dynamics of romantic reciprocality in a fully immersive VR application. Through a qualitative study, the first few chapters of the thesis examine a customizable social VR application ‘RecRoom’ as a dating technology medium to analyze how dimensions of interaction - including but not limited to voice, haptics, and spatiality - influence the dynamics of dating experiences. Tinder is employed as a contrasting chat-based medium to situate and deepen our learnings about dating in VR. The principal finding from our research is fully immersive VR allows for better, efficient, and effective ‘gauging’ of matches resulting in well-informed decisions to meet (or not) virtual partners in real life than existing chat-based mediums like Tinder. We base this conclusion on a combination of factors such as environmental alleviation of pressure, spontaneity, real-time interaction, better avenues of attraction leading to improved gauging of matches, and experience of first dates. However, the very aspects of fully immersive VR that make it a better medium to gauge matches open the floodgates to ethically mixed experiences - with online harassment taking a new form and inflicting genuine harm. The penultimate chapter endeavors to close the thesis by outlining the negative aspects of fully immersive VR, particularly harassment and raises the crucial need for the revision of ethical norms governing human- VR interactions. The goal of this chapter is to highlight the ethical implications of a medium capable of affording full bodily immersion, encouraging informed and responsible development of future VR dating apps.

Abstract

Learning computational models for visual attention (saliency estimation) is an effort to inch machines/robots closer to human visual cognitive abilities. Data-driven efforts have dominated the landscape since the introduction of deep neural network architectures. In deep learning research, the choices in architecture design are often empirical and frequently lead to more complex models than necessary. The complexity, in turn, hinders the application requirements. In this work, we identify four key components of saliency models, i.e., input features, multi-level integration, readout architecture, and loss functions. We review the existing state of the art models on these four components and propose novel and simpler alternatives. As a result, we propose two novel end-to-end architectures called SimpleNet and MDNSal, which are neater, minimal, more interpretable and achieve state of the art performance on public saliency benchmarks. SimpleNet is an optimized encoder-decoder architecture and brings notable performance gains on the SALICON dataset (the largest saliency benchmark). MDNSal is a parametric model that directly predicts parameters of a GMM distribution and is aimed to bring more interpretability to the prediction maps. The proposed saliency models can be inferred at 25fps, making them suitable for real-time applications. We also explore the possibility of improving saliency prediction in videos by using the image saliency models and existing work.

Abstract

Music signal processing is a sub branch in signal processing which is a promising area these days. Music analysis based on signal processing techniques paved a new way of generation and analysis of music. Music analysis and recognition of various swaras and ragas is inherent to human understanding. Music signal processing include various areas of research such as Synthesis of Music, Transcription, Classification, Music Information Retrieval, Raga Classification, Tala Classification, Instrument/ Voice Identification, Audio Matching, Source Separation, Tonic Identification, Intonation/melodic/rhythmic analysis, Music emotion recognition etc. The concept of Raga and Tala is integral part of Indian Classical music. Raga is the melodic component while Tala is the rhythmic component in the music. Hence, classification and identification of Raga and tala is a paramount problem in the area of Music Information Retrieval (MIR) systems. Although there are seven basic Talas in Carnatic Music, a further subdivision of them gives a total of 175 ragas. There are 72 melakartha ragas and more than thousand janya ragas. Statistical and machine learning approaches are proposed in Literature Survey to classify Ragas and Talas. However, they use complete musical recording for training and testing. As part of this thesis, a novel approach is proposed for the first time in Carnatic music to classify Carnatic music recordings using repetitive structure called Thumbnails. We proposed a parallel CNN-RNN models to classify Ragas and Talas in Carnatic music using ’Thumbnails’.