Abstract

In recent years, there has been a surge in finding vulnerabilities in browsers. This is mainly attributed to the ability of browser to host various services by gluing different applications. However, most of the research has gone into addressing web-related vulnerabilities like, XSS, SSL, SQLi etc. In this work, we focus on other type of vulnerabilities namely low-level or implementation level bugs. We specifically target embedded interpreters in browsers, as these software process codes that may come from a malicious site. Our contribution is to advance state of the art in interpreter testing. Fuzzing is an automated black box testing technique used for finding security vulnerabilities in the software by providing random data as input. Several frameworks for typical applications (e.g. network protocols, files formats, API‘s, applications and other targets) have been written so far. However, for interpreter testing, very few fuzzers exist. We present an automated evolutionary fuzzing technique to find bugs in JavaScript interpreters. However, in the case of an interpreter, fuzzing is challenging because the inputs are piece of codes that should be syntactically / semantically valid to pass the interpreter’s elementary checks. On the other hand, the fuzzed input should also be uncommon enough to trigger exceptional behavior in the interpreter, such as crashes, memory leaks and failing assertions. In our approach, we use evolutionary computing techniques, specifically genetic programming, to guide the fuzzer in generating uncommon input code fragments that may trigger exceptional behavior in the interpreter. We implement a prototype named IFuzzer to evaluate our technique on real-world examples. IFuzzer uses the language grammar to generate valid inputs. We applied IFuzzer first on an older version of the JavaScript interpreter of Mozilla (to allow for a fair comparison to existing work) and found 40 bugs, of which 12 were exploitable. On subsequently targeting the latest builds of the interpreter, IFuzzer found 17 bugs, of which four were security bugs.

Abstract

The thesis presents end-to-end frameworks for joint semantic and motion segmentation in outdoor dynamic scenes in both monocular and stereo setup. Dynamic scene understanding is a challenging problem and motion segmentation plays a crucial role in solving it. Incorporating semantics and motion enhances the overall perception of the dynamic scene. For applications of outdoor robotic navigation, joint learning methods have not been extensively used for extracting spatio-temporal features or adding different priors into the formulation. The task becomes even more challenging without stereo information being incorporated. The thesis proposes an approach to fuse semantic features and motion clues using CNNs, to address the problem of monocular semantic motion segmentation. We deduce semantic and motion labels by integrating optical flow as a constraint with semantic features into dilated convolution network. The pipeline consists of three main stages i.e Feature extraction, Feature amplification and Multi Scale Context Aggregation to fuse the semantics and flow features. Our joint formulation shows significant improvements in monocular motion segmentation over the state of the art methods on challenging KITTI tracking dataset. The focus in the second part of the thesis is temporally consistent joint semantic and motion segmentation. Segmenting moving objects in a video sequence has been a challenging problem and critical to outdoor robotic navigation. While recent literature have laid focus on regularizing object labels over a sequence of frames, exploiting the spatio-temporal features for motion segmentation has been scarce. Particularly in real world dynamic scenes, existing approaches tend to fail in segmenting moving objects with large camera motion. In this thesis, we present an approach for exploiting semantic information and temporal constraints in a joint framework for motion segmentation in a video. We propose a formulation for inferring per-frame joint semantic and motion labels using semantic potentials from dilated CNN framework and motion potentials from depth and geometric constraints. We integrate the potentials obtained into a 3D(space-time) fully connected CRF framework with overlapping/connected blocks. We solve for a feature space embedding in the spatio-temporal space by enforcing temporal constraints using optical flow and long term tracks as a least-squares problem. We evaluate our approach on KITTI Tracking benchmark and demonstrate results superior to the state-of-the-art in motion segmentation.

Abstract

Natural language processing is a field of artificial intelligence and computational linguistics that aims at bridging the gap between human beings and computers. It deals with processing natural languages in various forms like speech and text. Processing a natural language happens at various levels i.e, word, phrase, sentence, syntax, semantics and discourse. In this work we present our efforts at making advancements at syntax and semantic levels in dependency parsing. Dependency parsing involves discovering relationships between words in a given sentence so as to understand it better. We begin this thesis with a comparative error analysis of two parsers - MALT and MST on Telugu Dependency Treebank data. We discuss the performance of both the parsers in relation to the Telugu language and then talk in detail about both the algorithmic issues of the parsers as well as the language specific constraints of Telugu. We follow this by proposing a semi-supervised approach by introducing clustered word embedding based features into dependency parsing. This involves a simple and effective semi-supervised method to introduce features that incorporate dependency label clusters derived from a large annotated corpus into data driven dependency parsing. We demonstrate the effectiveness of the approach in a series of experiments on Hindi language data. We then propose a neural network based classifier voting approach to dependency parsing using multiple classifiers as component systems in an ensemble and a neural network algorithm as an oracle. We show significant improvements over the best component systems for both transition-based and graph-based dependency parsing.

Abstract

SiliconCarbide(SiC) is a wide band gap semiconductor with high thermal conductivity, high electron saturation velocity and high critical electric field properties. Silicon based devices, if replaced by SiC devices, could reduce the switching losses and improve the power conversion efficiency in grids becauseof their high frequency capability. However, the main limitation of SiC based MOS devices is thei r inability in handling higher electric fields at the oxide interface. An alternative to overcome this limitation is to replace the conventionally used SiO 2 with the high-K dielectrics that can sustain high electric fields. High-K dielectrics ZrO 2 and HfO 2 , with a dielectric constant of about 25, have been chosen for this study, as SiO 2 , with a dielectric constant of 3.9, cannot sustain high electric fields. This approach has been attempted earlier with HfO 2 as the gate dielectric in the case of SiC power MOSFETs. Previous studies of SiC MOSFETs with HfO 2 /SiO 2 stacking gate dielectric have shown significant improvement in the electronic performance. The electric field in the high-K dielectrics does not exceed the safe operational value, even when the underneath SiC has exceeds high electric field values. In order to address these concerns, an asymmetric trench gate SiC IGBT using high-K dielectrics such as HfO 2 and ZrO 2 as alternatives to the conventionally used SiO 2 has been studied. While supporting high electric field is one aspect, in this work it is investigated whether replacement of SiO 2 with the high-K dielectrics considered has any influence on the switching characteristics of IGBTs such as rise time, fall time, and turn OFF time using TCAD (Technology Computer-Aided Design) simulations. The static and switching characteristics of the SiC IGBT have been explored using technology based two-dimensional numerical computer simulations. ZrO 2 and HfO 2 exhibited better forward transcon-ductance ratio and reduced tail current thereby indicating improved power losses. The power dissipation curves were found to be consistent with the results obtained. However, these high-K dielectrics have low band offset as compared to SiO 2 -SiC interface. A lower band offset could lead to a high gate leakage current, which was resolved by introducing a thin layer of SiO 2 between SiC and high-K dielectrics. Power dissipation has been analyzed for IGBT with SiO 2 , HfO 2 and ZrO 2 layers by applying a pulse signal. In addition, the power dissipation is simulated for temperature range of 300-600K for different dielectric layers as the switching performance of a device is supposed to deteriorate with increase in temperature. HfO 2 and ZrO 2 demonstrated good temperature stability. The work in this paper is an attempt to correlate the power dissipation with the static and dynamic characteristics for the respective oxide layers.

Abstract

A brain template, such as MNI152 is a digital (magentic resonance image or MRI) representation of the brain in a reference coordinate system for the neuroscience research. Structural atlases, such as AAL and DKA, delineate the brain into cortical and subcortical structures which are used in Voxel Based Morphometry (VBM) and fMRI analysis. Many population specific templates, i.e. Chinese, Korean, etc., have been constructed recently. It was observed that there are morphological differences between the average brain of the eastern and the western population. In this thesis, we report on the development of a population specific brain template for the young Indian population. This is derived from a multi-centeric MRI dataset of 100 Indian adults (21 - 30 years old). Measurements made with this template indicated that the Indian brain, on average, is smaller in height and width compared to the Caucasian and the Chinese brain. A second problem this thesis examines is automated segmentation of cortical and non-cortical human brain structures, using multiple structural atlases. This has been hitherto approached using computationally expensive non-rigid registration followed by label fusion. We propose an alternative approach for this using a Convolutional Neural Network (CNN) which classifies a voxel into one of many structures. Evaluation of the proposed method on various datasets showed that the mean Dice coefficient varied from 0.844±0.031 to 0.743±0.019 for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state of the art methods on nearly all datasets, at a reduced computational time. We also propose an end-to-end trainable Fully Convolutional Neural Network (FCNN) architecture called the M-net, for segmenting deep (human) brain structures. A novel scheme is used to learn to combine and represent 3D context information of a given slice in a 2D slice. Consequently, the M-net utilizes only 2D convolution though it operates on 3D data. Experiment results show that the M-net outperforms other state-of-the-art model-based segmentation methods in terms of dice coefficient and is at least 3 times faster than them.

Abstract

Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness or unexpectedness. Trivia could be successfully employed to promote user engagement in various product experiences featuring the given entity. A Knowledge Graph (KG) is a semantic network which encodes various facts about entities and their relationships. We propose a novel approach called DBpedia Trivia Miner (DTM) to automatically mine trivia for entities of a given domain in KGs. The essence of DTM lies in learning an Interestingness Model (IM), for a given domain, from human annotated training data provided in the form of interesting facts from the KG. The IM thus learnt is applied to extract trivia for other entities of the same domain in the KG. We propose two different approaches for learning the IM - a) A Convolutional Neural Network (CNN) based approach and b) Fusion Based CNN (F-CNN) approach which combines both hand-crafted and CNN features. Experiments across two different domains - Bollywood Actors and Music Artists reveal that CNN automatically learns features which are relevant to the task and shows competitive performance relative to hand-crafted feature based baselines whereas F-CNN significantly improves the performance over the baseline approaches which use hand-crafted features alone. We also study the problem of relevance scoring of triples. We use the triples with “type-like” relations in which we employed a dataset with relevance scores ranging from 0 to 7, with 7 being the “most relevant” and 0 being the “least relevant”. The task focuses on two such relations: profession and nationality. We built a system which could automatically predict the relevance scores for unseen/new triples. Our model is primarily a supervised machine learning based one in which we use well-designed features which are used to a build a Logistic Ordinal Regression based classification model.

Abstract

Slow moving traffic in heavily populated cities, can many times result in loss of lives due to emergency vehicles not being able to reach their destination hospitals on time. Recent advances in the field of Intelligent Transportation Systems (ITS) makes it increasingly likely that vehicles in the near future will be equipped with advanced systems that allow inter vehicular communication. In this thesis, we assume the usage of such a system to optimize the lane level dynamics for an emergency vehicle (EV), traversing a multi lane stretch of road under a variety of traffic settings. In particular, we present the Fixed Lane Strategy (FLS) and the Best Lane Strategy (BLS) for EV traversal and perform an extensive agent based analysis to study their strengths and weaknesses. Through a series of experiments performed using the well-known traffic simulator SUMO, we could show that: (a) BLS performs better than SUMO strategy on all traffic settings we tested. (b) BLS performs better than FLS in settings that capture real-world traffic conditions involving congestion and uncertainties while FLS performs better in well-behaved conditions and (c) BLS was found to be the best strategy for the setting calibrated using real world data (obtained from NYCDOT).

Abstract

The behavior of discourse in a morphologically rich language such as Tamil has not been studied from a Computational Linguistics standpoint. A study of discourse in Tamil using a systematic framework such as PDTB would help identify the manner in which discourse features are realized in Tamil morphology. Added benefits of such a study include establishing the role of discourse in its relationship with other linguistic aspects, and facilitating cross-linguistic discourse analyses between language groups of differing morphological richness.

Abstract

Dynamic Programming(DP) is a powerful technique used for efficiently solving a vast set of complex optimization problems. It is widely used in areas of scheduling, string editing, packaging, bioinformatics, and inventory management [17]. Because of its impact and applicability across a wide range of domains, it is believed that DP will retain its importance in the future for science and engineering. This importance makes it one of the Berkeley 13 dwarfs [6]. It is thus not surprising that a lot of research attention is devoted in parallelizing DP problems. There are many classes of dynamic programming problems [11]. One of the important class is LDDP (Local Dependency Dynamic Programming). LDDP problems are ones in which update to an entry in the DP table is determined by the contents of neighboring cells [11]. Many non DP problems also mimic local dependency nature of LDDP problems. We collectively classify these local dependency problems (DP and non DP) as LDDP-Plus Problems. One more class of dynamic programming problems which is based on locality is HLDP (Horizontally Local Dynamic programming). HLDP class represents problems in which the contents of the cells from the immediately previous row/column decide the update to an entry in the DP table. Examples of both these classes of problems are common in many domains like speech processing (Dynamic Time Warping Algorithm [5], Non-Segmental Dynamic Time Warping [29]), bioinformatics (longest common subsequence, pairwise sequence alignment with affine gap cost [11]), image (Floyd-Steinberg dithering [15]) and the like. Many classical problems like 0/1 knapsack problem and checkerboard problem are also examples of these classes. Many other DP problems can also be modeled as LDDP / HLDP problems. Therefore, efficient parallel solutions to these two classes of problems is of immense research interest. While problem specific solutions are best way to achieve performance, generic solutions/frameworks enable the programmers and domain experts to extract reasonably good performance with little effort. This also enables them to take advantage of high performance computing platforms such as multi-core CPUs and GPGPUs with lesser effort. So it is meaningful to create generic parallel frameworks for solving a class of problems. The current architectural trend in parallel computing is towards a heterogeneous collection of devices involving CPUs and accelerators such as GPUs and Intel Xeon-Phi. Hence, the design and development of heterogeneous algorithms aimed at commodity heterogeneous computing platforms are of immense research interest. Heterogeneous algorithms for a variety of problems from domains such as sorting [7], graph algorithms [8] [16], sparse matrix computations [21], are reported in recent literature. In this thesis, we categorize LDDP and HLDP problems into various categories. We design and implement pure/heterogeneous algorithms for each of these categories of problems as a complete framework. Our framework can act as a software tool that can enhance the productivity of programming modern heterogeneous systems. Using our framework, users can readily create solutions for problems that follow LDDP or HLDP approach by just providing the function that dictates the dependency pattern.

Abstract

Proposition Bank or PropBank is a corpus in which the arguments of each verb predicate (simple or complex) are marked with their semantic roles [Kingsbury and Palmer, 2003]. PropBank establishes a layer of semantic representation in a Treebank already annotated with phrase structure or dependency structure. Adding the semantic layer through predicate-argument structure is quite challenging and these challenges can be comprehended from the fact that the syntactic structures in which a predicate’s arguments and its adjuncts are realized can vary based on the senses present in the sentence. Our effort of building a Proposition Bank for an Indian language, Urdu, [Anwar et al., 2016] spoken in major parts of India and Pakistan, describes the annotation process of labelling the predicate-argument structure on top of a Urdu Dependency Treebank [Bhat and Sharma, 2012]. The need for such a language resource arises from the fact that while Urdu Treebank does provide syntactico-semantic information, exploiting complete semantic information present in the form of predicate-argument structure in sentences will help in dependency parsing. Adding a semantic layer to the Dependency Treebank will help in addressing this requirement. The Urdu Proposition Bank is annotated by two annotators. With inter-annotator agreement statistics, we showed that there is almost perfect agreement between the two annotators which implies their analogous understanding of the annotation guidelines and of the linguistic phenomenon present in the Urdu language. We also introduce a statistical Semantic Role Labeler for Hindi and Urdu, two major Indian languages [Anwar and Sharma, 2016]. A Semantic Role Labeler automatically marks the arguments/valency of a predicate in a predicate argument structure of a sentence. The proposed system is based on supervised machine learning approach on Hindi and Urdu PropBanks which are being built for these languages [Vaidya et al., 2011] [Anwar et al., 2016]. Our approach is a 2-stage architecture in which, first the arguments pertaining to a predicate in a sentence of PropBank are identified by the system and finally those identified arguments are classified into one of the semantic labels of PropBank. Our system uses Logistic Regression machine learning algorithm and Support Vector Machines [Pedregosa et al., 2011] to predict and classify the arguments of a predicate respectively into one or more classes which happens to be the PropBank labels or semantic labels used to build the PropBanks. We show that our system has substantial precision of around 75% while identifying the arguments for Urdupredicates and 86% while doing so for Hindi. While classifying these identified arguments in semantic roles for Urdu and Hindi PropBank, our system reached a precision of around 83% and 58% respectively. We also experimented with automatic dependency parses extracted from dependency parsers and used them as features for our semantic role labeler. We wanted to observe how our semantic role labeler will perform when exposed to automatic dependency parses. As expected, there was a drop in the accuracy of the labeler when automatic parses were used as compared to when gold parses were used. To the best of our knowledge, this is the first such attempt of building a semantic role labeler for any Indian language. Past researches related to dependency parsing of Indian languages have shown that semantic information present in the sentences do provide important cues in parsing [Bharati et al., 2008] [Ambati et al., 2009] [Jain et al., 2013] [Bhat and Sharma, 2013]. After semantic role labeler for Hindi and Urdu was available, we wanted to study and observe the impact of semantic labels given by semantic role labeler in parsing. Therefore, we built an automatic pipeline using dependency parser and semantic role labeler. The semantic role labeler using automatic dependency labels gives semantic labels which subsequently are used as features for the dependency parser. The intuition behind building such a pipeline is that we wanted to exploit the accuracy and performance of parser when exposed to automatic semantic labels. We also worked on Inter-chunk Parsing and Intra-chunk Parsing. We performed several experiments to analyze the involvement of post-positions or case-markers in parsing. One such experiment included splitting the data-set available into 10 folds and analyzing the results of parser in each and every fold to get a better sight of performance of the parser.