Abstract

In-band full-duplex (FD) communication that enables concurrent transmission and reception in a single frequency-time channel holds the promise of nearly doubling the spectral efficiency compared to the current half-duplex and out-of-band FD communication systems. The main hurdle in the practical implementation of such a full-duplex system is the loopback self-interference (LSI), which arises due to the FD operation of the communicating nodes. In such an FD transceiver, the transmit signal generally overpowers the intended receive signal making its detection extremely difficult. However, with the recent advancements in the LSI cancellation techniques, FD systems have finally become viable. Hence, it is no wonder that FD has been envisaged as one of the promising technologies for the next generation wireless networks, viz., 5G and beyond. In practice, the LSI cancellation process cannot eliminate the LSI completely due to various reasons, chief among them the difficulty in obtaining the perfect channel state information of the loopback channel. As a result, we are still left with a residual LSI, which would degrade the performance of an FD system. In this work, we consider the design of optimal transceiver and relay processing algorithms for FD multiple-input multiple-output (MIMO) cooperative networks. Specifically, we propose the algorithms for FD communication in three different scenarios: (i) a cognitive radio network, (ii) secure communication using physical layer security techniques, and (iii) a cellular network. In the cognitive radio setup, we consider the FD transceivers and the FD relay to be a part of the secondary network. The transceiver and the relay precoders are designed so as to ensure that the interference caused to the primary user is always below a specified threshold. We have considered the maximization of the receive signal-to-noise ratio (SNR) as the criterion for the design of the transceiver precoders. We have further extended this problem to the case where multiple relays are available for selection. A central control unit selects the relay that provides the highest receive SNR. In the next scenario, a passive eavesdropper tries to decode the data of the two legitimate FD transceivers. In this case, we consider secure communication between the legitimate transceivers using physical layer security techniques. The precoder and receive filter matrices are designed to ensure secure communication between the legitimate users. The secrecy performance of this system in the presence of a passive eavesdropper is analyzed by considering the relay signal as artificial noise for the eavesdropper. The effectiveness of the proposed design is evaluated in terms of the sum-secrecy-rate of the system. Finally, we consider a futuristic FD cellular network scenario where an FD base station communicates with two FD transceiver nodes, where one of the transceivers is assisted by an FD amplify-and-forward relay. In each of the scenarios, our prime focus is on mitigating the residual LSI and its detrimental effect on the performance of the system. The precoders at the transceiver nodes are designed to mitigate the LSI based on the requirements of their operating environment. The LSI at the relay is mitigated by incorporating it into the sum of meansquareerror (SMSE) minimization problem. The relay precoder and the transceiver receive filters are designed based on the minimization of the SMSE of the end-to-end communication. The resulting optimization problem is non-convex and hence a globally optimal solution is not guaranteed. We apply the coordinate descent method and the Karush-Kuhn-Tucker (KKT) conditions to obtain sub-optimal solutions. The complexities of the proposed algorithms are lower than that of the state-of-the-art algorithms, in terms of both number of computations and memory requirements. The various simulation results demonstrate the efficacy of our proposed designs in terms of LSI mitigation, power efficiency, number of iterations, and sum-rate performance.

Abstract

Wireless communication has expanded its horizon in almost every aspect of human lives and its consequence is rapid surge in the demand of spectral resource. Electromagnetic spectrum is a gift of nature and such spectrum bands cannot be increased beyond certain limit. Presently, spectrum is licensed by government agencies to the interested parties by accepting a license fee. At the time of allocation, it is assumed that the licensed party will be utilizing the spectrum up-to its full potential i.e. 100% of the time. Hence the frequency bands to be utilized by the primary user are decided a-priori irrespective of its dynamic utilization status. On the other side with the advent of technologies like IoT, wearable consumer electronics and smartphones etc. applications are becoming bandwidth hungry and the number of connected devices are increasing drastically. Gardner technology research, a premier IoT research company claims that in the year 2015, 4.9 billion devices were connected to the internet and by 2020 this figure will reach to 20.8 billion. Hence with such an exponential increase in the number of connected devices, we will soon be facing scarcity of the electromagnetic spectrum in near future. Cognitive Radio is an innovative technology with unprecedented level of intelligence and capability to enhance the system capacity as well as spectrum agility of wireless communication systems. It mitigates the problem of spectrum scarcity with the aid of opportunistic access of spectrum. This work is dedicated towards developing hardware efficient and reconfigurable spectrum sensing architectures as well as techniques from VLSI design and implementation perspectives. We propose enhancements to the widely used energy detector design which helps in reducing the overall hardware resources (like adders and multipliers) consumed by 46% and alleviates the memory footprint by more than 95%. Further the conventional energy detector design is extended for wide band sensing applications without compromising on detector performance. We also present reconfigurable VLSI architectures for cyclostationary spectrum sensor which aids in sensing OFDM signals constructed with a variety of sub-carrier lengths. We suggested a novel selective sampling based reconfigurable cyclostationaryspectrum sensor which reduces the memory requirement by 99% and other resources by 33%. To evaluate the performance of these architectures, extensive computer simulations are carried out and detector architectures are implemented on an FPGA platform to verify the timing and functionality. Such verification has been carried out by feeding real-world RF signals captured from standard-RF SDR front-ends. Finally, ASIC syntheses and post-layout simulations of these architectures in 90 and 65 nm-CMOS technologies are carried out and its implementation results are compared with the stateof-the-art.

Abstract

An important component of software reliability is the assurance of certain security guarantees, such as absence of low-level bugs that may result in code exploitation, for example. A program crash is an early indicator of possible errors in the program like memory corruption, access violation or division by zero. In particular, a crash may indicate the presence of safety or security critical errors. A safety-error crash does not result in any exploitable condition, whereas a security-error crash allows an attacker to exploit a vulnerability. However, distinguishing one from the other is a non-trivial task. This exacerbates the problem in cases where we get hundreds of crashes and programmers have to make choices which crash to patch first! In this work, we present a technique to identify security critical crashes by applying machine learning on a set of features derived from core-dump files and runtime information obtained from hardware assisted monitoring such as the last branch record (LBR) register. We implement the proposed technique in a prototype called Exniffer. Our empirical results, obtained by experimenting Exniffer on several crashes on real-world applications show that proposed technique is able to classify a given crash as exploitable or not-exploitable with high accuracy.

Abstract

World Wide Web was brought to the fore in 1989 as the first version of HTML (HyperText Markup Language), accompanied with the capability of hypertext - a base format for publishing web pages. As the user types in a website’s address in the address bar of the browser, an HTTP request is initiated by the browser. The browser receives the response from the web server and builds a DOM (Document Object Model), a data structure for storing information in a tree format. It then renders the content from the DOM into its window. Further, Image tag was proposed in 1993, which enabled web pages to cascade requests thereby escalating security threats. The basic browser model, however, allows content inclusion from any server, and this prevails as a basic design flaw which has led to several malicious attacks. Existing browser security policies like Same Origin Policy (SOP), Content Security Policy (CSP) do not mitigate these attacks by design. On March 27, 2015, Github witnessed a massive DDoS attack, the largest in Github’s history till date which prevailed for over three days. In this incident, browsers and users were used as vectors to launch the attack. This class of attack has affected many other websites on a large scale, such as Incapsula and CloudFlare. Upon detailed analysis, we found the primary cause of these attacks to be cross-origin request attacks (CORA). Given the fact that current browser security model does not prevent content inclusion cross-origin attacks, it is rather easy to launch such attacks. CORA is one of the top attacks in web security with CSRF (cross-site request forgery) attack ranking in the top 10 attacks (2017), by OWASP. Against this compelling backdrop, this work undertakes a detailed analysis of CORP, a browser security policy which can be used to mitigate cross-origin request attacks. CORP enables a server to control cross-origin interactions initiated by a browser. The browser intercepts the cross-origin requests and blocks the unwanted requests by the server. Further, we explain the outcome of this work in three folds: Firstly, it demonstrates a compelling application of CORP in mitigating the browser-based DDoS attack, which has severely affected several websites in the past. Secondly, to gain a better understanding of CORP, we built a formal model of cross-origin request attacks and mitigated it using CORP. CORP has a client side dependency where users will have to use a CORP compliant browser to mitigate cross-origin attacks. Considering this, the concluding task was - implementation of CORP in Chromium source code. To evaluate the effectiveness of CORP, we conducted several experiments on various cross-origin attacks. We simulated a browser-based DDoS attack in a lab environment and did a detailed analysis on mitigation of the attack using CORP. After that, we modelled a complex case of a cross-origin transaction, FIM (Federated Identity Management), and incorporated a malicious cross-origin attack in it. We, then, validated CORP by mitigating the attack successfully using CORP. Modelling helped us in identifying the different parts involved in a CORP transaction. Thereon, to gain deeper insight into the functioning of CORP, we implemented CORP in Chromium browser and demonstrated the mitigation of various cross-origin attacks (CSRF, Clickjacking, cross-site timing attacks), present in websites hosted by top enterprises (such as facebook, google, twitter, dropbox, etc.), using CORP.

Abstract

In a large distributed network of interconnected nodes, the goal of any Secure Message Transmission (SMT) protocol is to securely deliver the sender’s message at the receiver’s end in the presence of a computationally unbounded adversary, that can partially control the network by corrupting some of its nodes (except the sender and the receiver). In the literature, different variants of SMT problem have been studied depending on the type of network model and/or the types of node corruptions. Undirected graph model and directed graph model are two of the most well-studied network models. Different types of node corruptions studied are passive, fail-stop, omission and Byzantine. In passive corruption, the adversary is only allowed to read/eavesdrop the corrupted node. In fail-stop corruption, the adversary can crash the corrupted node at its will. In omission corruption, the adversary can eavesdrop and/or crash the corrupted node. In Byzantine corruption, the adversary fully controls the actions of the corrupted nodes. In an undirected graph setting, for most variants of SMT problem whether the corresponding SMT protocols exist or not is known in the literature. Moreover, whenever a protocol exists, communication efficient protocols have been designed too. Whereas, in directed graph setting very little is known about the existence of protocols (for dif- ferent variants of SMT problem) as well as the design of efficient protocols. This thesis primarily focuses on bridging this gap between undirected and directed graph settings. It is usually difficult to design efficient protocols in arbitrary directed graphs due to the plethora of different cases/possibilities that usually need to be dealt with. However, in the literature due to the simplicity and ease of protocol design, wires model is considered which is a special case of directed graph setting. In wires model, paths (known as wires) from the sender to the receiver and vice-versa are considered whereas the weak paths are discarded. On the other hand, if the intermediate nodes can generate random-coins and/or perform computations, it is known that the directed wires-based abstraction is inadequate to capture arbitrary directed graphs. In this thesis, we design efficient protocols for different variants of SMT problem in both wires model as well as arbitrary directed graph model. Moreover, we introduce a new model namely routing model to answer the following fundamental question. If each intermediate node is a mere router (can not do computations but can route message to its neighbours) then under what condition(s) is given variant of SMT (im)possible? Furthermore, in arbitrary directed graph setting, we design a protocol which is both communication as well as round/hop optimum when the adversary is passive.

Abstract

Indian classical music is monophonic in nature. Melody is key to any performance. The entire performance is based on a single raga (melodic mode). Some performances are based on a coalescence of two or more ragas. Only a small duration in any musical performance in Indian art music is the precomposed portion. The rest involves a gradual systematic exploration of the raga using various forms ¯ of improvisation. The performer has great freedom in exploring his own improvisational patterns in order to delve into the intricacies of the given raga. However the performer is bound by the frame- ¯ work of the raga, t ¯ ala and other factors such as ghar ¯ an¯ a (the musical school which he belongs to), laya ¯ (tempo). There is a great variation in quality and nature of improvisation among different practitioners based on their cognitive and imaginative abilities. There are two important tools used in improvisation: alankar/alank ¯ aran (musical ornamentation) and t ¯ anas. In this thesis we restrict our study to the present ¯ day usage of alankaran ¯ 1 . Ornamentation is an indispensable part of improvisation. The performer tries to evoke the mood of the raga (melodic-mode) with the delicate exploration of flourishes and ornamentation. Indian classical ¯ music is cognitively associated with these decorations instead of the actual underlying notes. We are interested in discovering the hidden musical patterns and structures in Indian music which are fundamental in unfolding of a raga. Recognizing these patterns can further help in music search, retrieval, recommendation and pedagogy. This thesis makes an attempt to study these ornaments computationally. We begin the study with the objective to computationally detect these ornaments in Dhrupad performances on rudra veen ¯ . a. Dhrupad is the oldest surviving form of Hindustani classical music, is monophonic ¯ in nature and has a wealth of ornamentation. We have crowd-sourced multimedia annotations of primary alankara-s, namely, meen ¯ . da (glides between notes), andolana (vacillations in the note region) and ¯ gamaka (two notes alternation). These were used as the ground truth. Two signals in the time domain, a query signal, which consists of fundamental ornament templates, and a reference signal were compared. Using a variant of dynamic time warping, we then detect the matching subsequences in the reference signal with high accuracy. This method of iterative template matching is naive and has drawbacks, some of which are: 1.) Restrictiveness of the experiment to a set of non-exhaustive list of ornament templates, 2.) Computational overheads of comparing huge pitch vectors using dynamic time warping. While DTW matching was an effective method of detecting these ornaments, we didnt use any domain specific knowledge to detect/identify these ornaments computationally. Cognitively ragas are ¯ 1Most of these definitions are either explained in the introduction section or in the glossary at the end vi vii associated with ornamentation.These along with the ways in which notes are approached,sustained and quitted constitute flows. We call them flows due to the constant change in the frequency and amplitude domains. The rate of change in frequency and amplitude domains together constitute the fundamental nature of ornamentation. We thus model ornamentation as first and second derivative of frequency and amplitude information to see whether the property of rate of change is fundamental to the idea of flows. We were able to achieve very high clustering accuracy by using this representation and our custom defined distance measure. We achieved an accuracy of 81% in distinguishing between Murki vs Kan and 84.5% in Andolana vs Ghaseet. This again shows that transition is at the heart of the definition ¯ of flows and ornaments. However this doesnt account for hidden musical patterns which might not be captured by the manually engineered representation and custom distance. It is important to note that ornaments and flows are intricate Indian music decorations which only experienced musicians can recognize and comprehend. In all of our previous experiments we have used small annotated datasets which do not exhaustively capture patterns underlying ornaments. The biggest challenge in learning and making sense of flows or ornaments is the unavailability of annotated music data. Thus, to have a big data driven exhaustive exploration of these melodic structures implies having either 1.) a multi performer crowdsourced effort to create a large exhaustive dataset, or 2.) an unsupervised/semi-supervised method of learning. To avoid the shortcomings of non-exhaustive small datasets and hand engineered representations, we also develop a robust unsupervised method to learn fixed length representations of ornaments. In the domain of speech processing, neural networks have recently successfully been used for encoding acoustic inf

Abstract

Although tremendous progress has been achieved towards understanding the biochemical versatility and biological functions of RNA, there is a need to deepen our understanding of the principles governing the structural and functional diversity of these fascinating biomacromolecules. One possible factor that contributes to the structural diversity of RNA is the presence of a variety of post-transcriptional modifications of the canonical nucleobases. Detailed analysis of the role of noncovalent interactions involving modified nucleobases in RNA is thus, expected to contribute to our understanding of the factors governing the structure and dynamics of RNA. In an attempt to contribute towards this theme, the present work employs a combination of sequence and structural bioinformatics techniques, along with quantum chemical calculations to understand the structure and strength of hydrogen bonded pairs and higher order structures involving post-transcriptionally modified nucleobases that are present in RNA macromolecular crystal structures. In addition to highlighting the important structural roles some of these hydrogen bonded entities in RNA molecules, our studies highlight the need for, and providing a comprehensive approach towards further investigations on such interactions in the context of many biological processes involving RNA. In addition to understanding the role of posttranscriptional modifications in RNA, we explored the structural and functional diversity exhibited by riboswitches, non-coding RNA, a class of RNA molecules that are a part of RNA regulatory networks, which specifically binds to small cognate ligands and regulate their turnover. Using QM and MD simulations, we explored the molecular features responsible for ligand recognition, and the associated role of noncovalent interactions in gene regulation by riboswitches. Overall, the thesis contributes towards understanding the structure-functional relationships in RNA macromolecules.

Abstract

This thesis discusses a mixed-initiative, domain independent dialog system based on a hierarchically structured knowledge base. The system is rule-based and uses dependency relations and part-of-speech tags obtained from the Stanford Parser coupled with the hierarchical structure of the knowledge base to identify the user’s goal. Primarily, the system is able to accept multi-sentence inputs, that is, multiple sentences forming a query in a single user turn. The system was tested for its accuracy over answering questions, and also subjective testing was done to evaluate the dialog flow; mainly over the books domain. We show examples of the system developed over the domains of books, movies and restaurants to demonstrate the domain independence. We also discuss in detail the simple, rule-based system of extracting relevant information from a user’s query that contributes to the domain independent dialog system. Relevant information is extracted from a user query by using only dependency relations and POS tags, obtained from the Stanford Dependency Parser. Using the universal dependency tags provided by the parser, we tried to understand the semantic structure of a query. This is done by looking for semantically important dependents of the verb, such as the subject, direct object, prepositions and their objects, and so on. Using information obtained from a combination of the dependency relations and the inherent semantic implications of words (such as ‘who’ or ‘where’), we tried to extract the main objective or keyword of the query, and the constraints pertaining to it. This implementation itself is domain independent, however, a mapping to a knowledge base would require some domain knowledge; this issue is resolved using the hierarchically structured knowledge base. This keyword and constraint identification system is tested on the course management domain and the library domain. In order to further improve the domain independent keyword identification for natural language queries, we used statistical methods. We took queries supplemented by only their dependency tags (Stanford Parser) and part-of-speech tags (Stanford POS tagger) and labeled the keywords. We then delexicalised the training data, and used the Conditional Random Fields algorithm to learn these labels. We used the queries created by [45] in the course management domain for training, and tested our model on the queries of three domains: course management, library and the G EO Q UERIES 250 dataset and report fairly high accuracies of 90.65%, 83.19% and 97.13% respectively, making our model a truly domain independent and highly accurate keyword identifier. There is no agreed upon standard for the evaluation of conversational dialog systems, which are well-known to be hard to evaluate. The difficulty lies in pinning down metrics that will correspond to human judgments and the subjective nature of human judgment itself. We explored the possibility of using Grice’s Maxims to evaluate effective communication in conversation. We collected a few system generated dialogs from popular conversational chatbots across the spectrum and conducted a survey to see how the human judgements based on Gricean maxims correlate, and if such human judgments can be used as an effective evaluation metric for dialogs.

Abstract

Past decade witnessed an explosive growth in the amount of unstructured data, especially in the public domain, mainly due to Web 2.0 and social media. This has created a need for applications that extract structured information from such noisy data. Automatic extraction of structured information from unstructured data is called information extraction. Structured information thus extracted is stored in a Knowledge Base. The knowledge base stores facts about entities like name, type and other attributes. On the one hand, the information extraction task utilizes the facts of the entity stored in knowledge base to refine the extraction process, while on the other, the facts extracted refines the knowledge base facts further. Thus knowledge base provides structure and guidance to the extraction task, and gets enhanced by the results of the extraction task. Here we see that the tasks of entity extraction and knowledge base enhancement are mutually dependent and mutually beneficial. Hence this thesis proposes methods to enhance both the tasks, in an effort to build a strong and sound named entity extraction system. Documents typically talk about multiple named entities. All the named entities mentioned in the document are not equally important to the content of the document. Named entities that are important to the document are called salient named entities. In this thesis we propose a method to identify salient named entity of the document. Importance of a named entity can also be judged by understanding how the named entity is semantically related to other named entities mentioned in the document. We propose a method to identify the presence of such semantic relations within named entities. For example, semantic relation like attribute or category in a product title. Understanding the salience of a named entity and its semantic relations help the Named Entity Extraction task in extracting the important information from text, while filtering out unimportant information. Performance of Named Entity Extraction methods depend on the size and structure of the context of the named entity mention in the text. While bigger size and better structure of context results in improved performance of the Named Entity Extraction, lower size and poor structure of context results in reduced performance. We propose three different Named Entity Extraction approaches tailored to the varying size and structure of the context in this thesis. The proposed methods perform on par with state-of-the-art methods with improved latency. Named Entity Extraction approaches that work on lesser context and poorer structure, increasingly depend on non-textual signal like global coherence of entities in the knowledge base. Conventional EL performs well for popular entities but performs poorly for less popular (a.k.a tail) entities, because conventionally EL methods depend on richness of entity neighborhood in the Knowledge Graph (KG). In this context ‘KB’ refers to Knowledge Base like Wikipedia and ‘KG’ refers to Knowledge Graph like Wikipedia Hyperlink Graph. Tail entities have sparse entity neighborhood and hence EL methods perform poorly on them. In this thesis we propose ELDEN, an EL system that overcomes the degree sparsity problem of tail entities. ELDEN enriches entity’s neighborhood in a KG by extracting high quality mentions of entity from a web corpus using Pointwise Mutual Information (PMI) measure. ELDEN outperforms state-of-the-art EL systems while achieving significant improvement in linking tail entities, achieving best results on CoNLL and TAC datasets. We follow up the discussion on ELDEN with a discussion on information retrieval task that is improved by use of KB entities. We propose a novel method for enhancing classification performance of research papers into ACM computer science categories using KB entities, both Wikipedia and Freebase entities. All through this thesis we present five methods of improving Named Entity Extraction using Knowledgebases. We conclude the thesis by looking at how Knowledgebases can be improved with Named Entity Extraction. We review and analyze the main approaches of New Entity Identification (NEI) in Named Entity Extraction systems. We analyze the features and share insights from reproducing state-of-the-art results, suggesting future improvements.

Abstract

The Cooperative Target Observation (CTO) problem has been of great interest in the multi-agents and robotics literature due to the problem being at the core of a number of applications for example surveillance, inspection and search-and-rescue. In CTO problem, the observer agents attempt to maximize the collective time during which each moving target agent is being observed by at least one observer agent in the area of interest. The area of interest can include airport, border patrolling or public events. Micro-drones are typically used for such operations as with large gatherings and protests, or even when monitoring individuals, it is often difficult to maintain an overview. They can be pretty useful for surveillance allowing for a (top) view of the happenings on ground. The CTO problem in most of the prior works is solved by finding the best suited strategy for observer agents, considering the target agent’s movement to be Randomized. Given our focus on surveillance domain, we modify this assumption to make the targets strategic and present two target strategies namely Straight-line strategy and Controlled Randomization strategy. We then modify the observer strategy proposed in the literature based on the K-means algorithm to introduce five variants over the K-means algorithm and provide experimental validation across the strategies. In surveillance domain, it is often reasonable to assume that the observers may themselves be a subject of observation for a variety of purposes by unknown adversaries whose model may not be known. Randomizing the observer’s action can help to make their target observation strategy less predictable, so that the unknown adversary cannot harm the observer agent. As the fifth variant, we therefore introduce Adjustable Randomization into the best performing observer strategy among the four variants over K-means algorithm, where the observer can adjust the expected loss in reward due to randomization depending on the situation.