Abstract

Drought is a natural hazard with a significant impact on the economy, agriculture, and environment. It is defined as a significant decrease in water availability in all its forms. Droughts are estimated using drought indices. Drought indices are numerical measurements that describe the severity of drought by combining data from one or more variables (indicators), such as precipitation and evapotranspiration (ET), into a single number. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, which take both precipitation, potential and actual evapotranspiration into account. Since ET is a critical factor in estimating drought, well-grounded ET estimations are required. The major forms of ET are Potential evapotranspiration (PET) and Actual evapotranspiration (AET). PET is described as the loss of water from a significant area that is equally covered with short, green crops that are actively growing. PET is regarded as the maximum amount of evapotranspiration. AET is defined as the total amount of water used in evaporation and transpiration by a crop during the entire growing season. Various methods have been developed to estimate PET and AET depending upon the availability of hydro meteorological variables. Various empirical based methods and hydrological model based simulations of PET and AET have been developed. Advancement of data-driven algorithms also have been extensively developed to estimate ET. In this context, many studies used empirical based estimates of ET for the calculation of drought indices. None of these studies analyzed the sensitivity of various ET methods to assess the drought characteristics. Thus, the present study aimed to include various PET and AET methods in the drought characterization. Various empirical methods, such as Penman-Monteith, Hargreaves, Turc, and Priestley-Taylor and data-driven method of Artificial Neural Network (ANN) has been used to estimate PET. The input variables used to estimate these methods are temperature, wind speed, solar radiation, and relative humidity which are obtained over the period of 1965 to 2015 for Hyderabad station. ANN model was trained and tested with climate variables as input variables and various empirical models as reference models to predict the best PET method. Penman-Monteith, Hargreaves, and Turc method performed better with ANN model estimates. Later, to study the impact of various empirical based PET estimates on drought estimation, SPEI is calculated using various PET estimates at different time scales. All other methods are compared with the Penman-Monteith method, which is considered the standard method because it considers the main meteorological factors. Hargreaves and Turc methods performed better with the standard method and these methods can be useful in estimating drought when minimum data is available. To assess the drought events accurately by various drought indices it is necessary to predict the hydro-meteorological variables (PET and AET) precisely. There are several challenges in estimating AET and PET at the fine spatial resolution. There are various empirical models (Budyko, Penman- Monteith, Hargreaves, and Turc) for estimating AET and PET. Still, these empirical methods does not account for the catchment characteristics, which may underestimates the actual amount of hydrological variables. Further, satellite-based remote sensing data are accessible for extracting evapotranspiration (ET) values. It provides global coverage and continuous observations of land surface variables affecting ET. Another conceptual based approach to estimate PET and AET at catchment scale is hydrological model such as Soil and Water Assessment Tool (SWAT). The present study aimed to include various approaches of empirical (Budyko, Penman-Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing in the drought characterization using SPEI and SPAEI. Remote sensing PET and AET are considered as standard methods to compare both empirical and modeled PET and AET estimates. The present methodology was tested on a dry-sub-humid river catchment of India, the Tungabhadra River catchment. It is recommended to use PET instead of AET when estimating drought indices as SPEI values performed relatively better than SPAEI. In the present study it is observed that although PET and AET estimates vary with different models, drought indices SPEI and SPAEI are not differing much at annual scales. Hargreaves and Penman-Monteith performed better results compared to remote sensing method in SPEI calculations. And for SPAEI, Budyko and Turc performed better results. Thus, the present study concludes that empirical models correlated better with the remote sensing data. The study will be prominent for ungauged river basins, where detailed hydrological data is limited and difficult

Abstract

To perform network forensic analysis on an incident in software defined networking (SDN), logs are of the utmost importance. Without any logs, an investigator will not be able to complete and justify the forensic analysis. With the advancement of network and communication technologies, the volume of logs that needs to be collected and retained is growing exponentially big. The network administrators face the problems in maintaining such huge logs for longer period of time for forensic analysis. Network Providers rely on purging data based on a certain stipulated duration expected by the local rules for forensics evidence. SDN providers have limitations in managing such big data since the expense involved is commensurate with the duration of data retention. Here, in this work we propose a novel idea to reduce and summarize large forensic data sets for faster querying as well as reducing its space complexity by using bloom filters. Through this work we aim to propose a system which can deliver more optimized forensic data availability in SDN platform compared to existing systems.

Abstract

In recent years, sentiment analysis has gained popularity as it is essential to moderate and analyse the information across the Internet. It has various applications like opinion mining, social media monitoring, and market research. Aspect Based Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level. ABSA classifies sentiment with respect to each aspect to gain greater insights into the sentiment expressed. Significant contributions have been made in ABSA, but this progress is limited only to a few languages with adequate resources. Telugu lags behind in this area of research despite being one of the most spoken languages in India and an enormous amount of data being created each day. In this thesis, we create a reliable resource for aspect based sentiment analysis in Telugu. The data is annotated for three tasks namely Aspect Term Extraction, Aspect Polarity Classification and Aspect Categorisation. Further, we develop baselines for the tasks using deep learning methods demonstrating the reliability and usefulness of the resource. In addition to this, experimentation has been done with transformer models, as they have led to pivotal changes in the field of NLP. Specifically, the setting of monolingual and multilingual models has been chosen to compare the effective contribution of the dataset created for ABSA in Telugu.

Abstract

Although the concept of a knowledge graph has been discussed since at least 1972 [93] it wasn’t until Google [96] unveiled their version in 2012 that it truly took off. Since then, several companies have started working on their own knowledge graphs, including Google, Amazon [54], eBay [82], Twitter, IBM [21], LinkedIn [37], Microsoft [95], and Uber. There has been a rise in the number of academic publications devoted to the topic of knowledge graphs in recent years, reflecting the growing interest in this idea. There are several books [79, 85, 52] and papers [84, 24] that detail knowledge graphs, as well as unique techniques to generating and analysing knowledge graphs and assessments of various knowledge graph aspects [103]. Apart from these enterprise Knowledge Graphs which are private and not accessible to public, there are a number of a number of public knowledge graphs published where the content is accessible for users of the web. The publicly available Knowledge Graphs are called open KGs. DBpedia [58], YAGO [40], Freebase [101], Wikidata [8] are the top few examples of such open KGs which are multi-domain and are built using the data from Wikipedia. Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work. Over recent years, document similarity has grown to become the foundation of various natural language processing activities, which are crucial to information retrieval, automatic question answering, machine translation, dialogue systems, and document matching. For document or topic similarity, focusing on the semantics within the text has been the most common and pursued direction of effort Our novel KG-based similarity classifier works on the limitations of previous approaches and also provides reasoning behind the classification. Our results show that we’re able to score similarity between Wikipedia documents accurately. Furthermore, the accuracy of our approach is able to withstand against noise and paraphrasing. We also see that our classifier can be used for category outlier detection in DBpedia. In this thesis, we will focus on Knowledge Graph construction from unstructured text and document similarity. Our knowledge graph construction approach performs better > 25% better than Cosine & Rule-based approaches, and is also computationally cheaper than state-of-the-art T2KG [53] by a magnitude of atleast 106 . We use these constructed knowledge graphs to classify similarity between two documents, which is used to detect category outliers in DBpedia and find highly similar documents.

Abstract

Urdu writing system is derived from the Persio-Arabic writing systems and thus it has adopted similar orthographical and morphological characteristics as that of Persio-Arabic languages. The first and foremost task for most of the NLP applications is Word Segmentation which involves identifying the bounding boundaries of words in written text. It is quite crucial to accurately identify the boundaries of each word in written text because all the downstream tasks in NLP are dependent on it, thus making Word Segmentation fundamentally important. Urdu adopts a continuous writing style which does not have an explicit and clear marker for word boundary. Furthermore, the inherent non-joining attributes of certain characters in Urdu create spaces within a word while writing in digital format. Thus, Urdu not only has space omission but also space insertion issues which make the word segmentation task challenging. We have studied and categorized the various issues that are observed with respect to the inconsistent usage of space character in Urdu script along with the orthographic and morphological reason behind it. Another challenge in computational processing of Urdu is the lack of benchmark resources and corpora for Word boundary identification. Leveraging the learning from the orthographic study of Urdu writing system, we have built a benchmark corpus for Urdu Word Segmentation, with an exercise of manual annotation, using white space as word boundary and Zero-Width-Non-Joiner (ZWNJ) character as sub-word boundary. A Conditional Random Field based sequence modeler was then used to train a character-level label prediction of a sequence of Urdu characters. Our model achieved state-of-the-art results with an F1 score of 0.98 for word boundary identification. Furthermore, we have applied our word segmentation model on studying the sociological phenomena of Diglossia in Urdu

Abstract

Gas sensors find widespread applications from healthcare to industrial safety. The detection of volatile organic compounds like acetone and ethanol in their gaseous state is very important, especially in the biomedical field. SMO-based gas sensors, which have simple architecture and several favorable features, require additional treatments like noble metal loading onto the SMOs like ZnO surface for superior performance. Experimental trial-and-error approach to performance improvement has been the prevalent practice due to the lack of knowledge of the electronic and chemical properties of such systems. Computational studies can play significant roles as predictive tools in this field. In this work, by using DFT study, we explore how Cu, Ag, Au, or Pd nanocluster loading onto ZnO cluster affects acetone vs. ethanol sensing performance of ZnO semiconductor. DFT simulations have been used obtain the chemical and electronic properties of various nanoclusters systems, which have been associated with sensor performances. First, we have studied the binding of acetone and ethanol to pristine (ZnO)2 cluster which explains how ZnO show better sensor response to acetone than ethanol. Next, we have explored the metal nanocluster M6 (where M = Cu, Ag, Au, and Pd) loading onto ZnO. Since the actual working motifs in the nanostructured sensor materials could be a small cluster of few atoms, we have taken M6 nanocluster as a model for the metal nanoparticle. Finally, we have investigated acetone vs. ethanol sensing performance of the metal nanocluster loaded ZnO systems. For each system, we have calculated various parameters like the binding energy, HOMO-LUMO energy and bandgap, Mulliken charge distributions, bond distance, etc. The study shows that the electronic structure effects of M6/ZnO nanoclusters can be used to understand the molecular level mechanism of sensor activation by metal nanoclusters and explain their acetone vs. ethanol sensing performance. A better understanding of the roles of various components of the sensor materials will help in better selection of sensor materials and develop efficient, low cost and portable sensors.

Abstract

The idea behind building a road surface and condition monitoring system is that in a developing country like India, we would like to create a sustainable system that can, with the help of geo-citizens, monitor road quality. This task is crucial to the municipal and city authorities to conduct as there is not enough manpower to monitor large road networks of the growing metropolises in the country. This semi-automated location-aware system can augment and help provide critical inputs to infrastructure/road managers in their tasks and deliver results like no other system currently in use. A crucial aspect of urban infrastructure management in cities is the continuous monitoring and maintenance of roads. Specific departments employed by governing authorities track road deterioration and ensure timely repair and maintenance of roads. Traditional methods in this domain use high-power laser or radar sensors and thereby go on to become intricate and demanding. Apart from the methods in data collection, the process of gathering, assimilating, and post-processing of data is an expensive and time-consuming task with low coverage and often in need of assistance from experts. Additionally, frequent data collection is essential to generate efficient and accountable results. High-end systems are inefficient in covering large areas at frequent and/or regular intervals of time, which leads to complexity in activity planning. In previous work, we have established that smartphone sensors can be used to determine the surface type and condition of the road. In the current thesis, we use the same algorithm for classification and build over it to develop a system that can address these above-listed challenges in an ever-expanding urban environment. MAARG system consists of three subsystems: Data Collection, Data Processing, and Data Analytics and Visualization subsystems. The mobile application developed serves as a data collection medium for raw data collection and training data collection. Geo-citizens can upload the data collected to a central server. The uploaded data is verified offline and then pushed to a processing server. The processing server runs the classification algorithm and labels it. This data is fused together and divided into logical segments. A majority voting and amalgamation algorithm then consume this data and arrives at the final output that is saved in the database. MAARG system includes a WebGIS dashboard comprising a back-end Express server that analyses and aggregates information from multiple inputs and showcases it on a browser with the OpenStreetMap as a background layer (WMS). While the proposed system not only uses tracking information but also image information to give an optimized understanding of the road infrastructure. Hence, MAARG processes positional information and attaches an on-the-fly analysis of the data collected from different sensors. This is achieved by collating multiple users data efficiently. This acts as an input to the query engine and visualizer where the users can visualize different spatiotemporal queries. MAARG system is an innovative, collaborative system for road condition monitoring leveraging the existing technologies by information fusion approach. Derived from geo-citizenship, we envision an ecosystem operating through a mobile application on smartphones of geo-citizens for large data collection and executing centralized processing. Prime advantages of such a platform are the multiplicity of data points and the crowd-sourcing model. Data collection from numerous sources ensures statistical advantage and expandable coverage for the monitoring system. Crowd-sourcing model provides reliable coverage and eliminates the need for deploying specialized road monitoring tools. This system is also dynamic to meet the increasing demand and prioritizes the areas of high usage. Overall, the MAARG system can disrupt the existing traditional methods and serve as an alternate innovative solution. This thesis primarily presents the architecture and system design of the three subsystems of the road monitoring system.

Abstract

Biometrics are physical and behavioral traits, which are unique and specific to individuals. Some of the widely used physical traits include face, fingerprint, iris, and behavioral traits include voice, signature, typing rhythm, and gait. Identifying a person based on any physical and behavioral traits is referred to as biometric authentication. Biometrics authentication can be more convenient and secure than passwords, because biometric traits are relatively fixed and cannot be easily stolen or shared. However, biometrics cannot be recovered and lost forever when compromised. Attackers strive to subvert the biometric systems and gain unauthorized access to digital and physical assets. Attacks on biometric systems can be classified into impersonation and obfuscation attacks. These attacks are the result of the following biometric vulnerabilities (i) An attacker can exploit a compromised template database either, to replace a template with an imposter template or, to present a stolen template directly to the matching module, (ii) Invertible transform function could lead to the estimation of biometric features, which can be used to create a physical fake or spoof of the biometric, and (iii) Attackers also exploit a higher false accept rate to impersonate a victim user, and (iv) Finally, biometric systems are sensitive to carefully crafted perturbations to the input biometric data. These perturbations can be used for impersonation attacks, as well as obfuscation attacks. Researchers proposed several template protection methods to overcome some of the above vulnerabilities of biometric systems and to defend against adversarial attacks. A template protection method converts an original template into a protected template in a non-invertible manner, intending to protect the biometric identifier even if a template is stolen. An ideal template protection mechanism must meet the following requirements: (i) Security or non-invertibility, (ii) Revocability, (iii) Diversity, and (iv) Matching performance. This thesis addresses some of these issues and proposes methods for facial template protection and for defending adversarial attacks on facial verification systems. The following studies were conducted in this thesis. (i) A modular Siamese network based method is proposed to improve the robustness of the face verification systems against adversarial attacks and simultaneously provide interpretability. In this approach, facial feature representations for each individual facial part such as eyes, nose and mouth are learned in latent space through feature disentanglement. (ii) A template protection method based on deep neural

Abstract

Quantum Computation is one of the most promising futures in furthering computing power and increasing the computational scientific simulation ability. However, the present-day quantum devices suffer from several limitations, a small number of qubits, limited connectivity, noisy evolution, and others. Therefore, the need of the hour is to come up with both hardware-based and algorithmic changes to mitigate these limitations and put forth a step toward a quantum computer that achieves supremacy over its classical counterpart. The primary focus of this dissertation is to present a method of efficiently compiling quantum circuits on present-day hardware to minimize the effects of limited connectivity and effects of noise. Further, we explore a class of hybrid quantum-classical algorithms called variational quantum circuits and attempt to characterize their properties, evolution, and advantages. First, we provide the requisite background in quantum computing, variational quantum methods, deep learning, and reinforcement learning. Next, we present qRoute, a reinforcement learning based solution for compiling quantum circuits onto present-day hardware. We elucidate the method that QRoute uses for depth minimized compilation, which is essentially a Monte-Carlo tree search put together with a graph neural network to decide which parts of the tree to explore. We discuss the details of the algorithm, the key points of innovation that differentiate it from the previous methods, and the state-of-the-art results it achieves on various circuits compilation benchmarks. Finally, we move to the application of quantum computers in solving real-world problems and discuss the circuits called variational quantum circuits. We present a framework qLEET for characterizing the training paths, loss landscapes, entanglement capability, and expressibility of these circuits, and we provide a use-case example in analyzing an algorithm called QAOA for computing the Max-Cut of a graph, which is an NP-complete problem and require time exponential in the number of nodes on a classical computer. All code for qRoute and qLEET has been released to the open-source community and provides easy and modular access to endpoints where our algorithms can be tweaked for further research in this domain.

Abstract

Model checking is now a standard technology for verifying large and complex systems. While there are a range of tools and techniques to verify various properties of a system under consideration, in this work, we restrict our attention to safety checking procedures using explicit state space generation. The necessary hardware resources required in this approach depends on the model complexity and the resulting state transition graph that gets generated. This cannot be estimated apriori. For reasonably realistic models, the available main memory in even high end servers may not be sufficient. Hence, we have to use distributed safety verification approaches on a cluster of nodes. However, the problem of estimating the minimum number of nodes in the cluster for the verification procedure to complete successfully remains unsolved. In the thesis, we propose a dynamically scalable model checker using an actor based architecture. Using the proposed approach, an end user can invoke a model checker hosted on a cloud platform in a push button fashion. Our safety verification procedures automatically expands the cluster by requesting more virtual machines from the cloud provider. Finally, the user gets to pay only for the hardware resources he rented for the duration of the verification procedure. We refer to this as Model Checking as Service. We approach this problem by proposing an asynchronous algorithm for safety checking in actor framework. The actor based approach allows for scaling the resources on a need basis and redistributes the work load transparently through state migration. We tested our approach by developing a distributed version of SpinJA model checker using Akka actor framework. We conducted our experiments on Google Cloud Engine (GCE) platform wherein we scale our resources automatically using the GCE API. On large models such as anderson.8 from BEEM benchmark suite, our approach reduced the model checking cost in dollars by 8.6x while reducing the wall clock time to complete the safety checking procedure 5.5x times