Abstract

Natural languages are ambiguous at various levels of processing e.g. phonological, morphological, syntactical and semantically (Jurafsky and Martin, 2000). While the human brain is able to process language despite its ambiguity, computer systems face problems in dealing with ambiguity. In the context of natural language processing, machine translation, information extraction and Q&A, the study of relation is very important as it helps to understand the structure of language and find the correct relation for genitive constructions. Anchoring this research to study of relations, this study develops rules based on semantic and syntactic information to disambiguate the parsing of recursive genitive constructions. Hindi follows “Head-Last” rule (Christiansen and Devlin, 1997) where the last noun behaves as the head noun and the preceding nouns act as the modifiers of the head noun. Since in recursion, we have more than one preceding noun, there is ambiguity about which noun(s) is/are modifier(s) of the head noun. This research is set out to handle the ambiguity arising due to recursion in Hindi language. In this work, we limit ourselves to examine recursive genitive construction with maximum two modifiers occurring before the head noun. We argue that semantic and syntactic information can be used to disambiguate parsing of recursive genitive constructions. We develop six syntactic agreement rules and four semantic rules for parsing the construction under consideration. We implement the rules and achieve an overall accuracy level of 86.6%. In the process of developing semantic rules we identify some semantic relations that exist between noun and its genitive modifiers. In the present work, we postulate 35 semantic relations for noun-noun and five for noun–verbal noun. We utilize these relations to develop the semantic rules which are used to determine the correct parsing of recursive genitive constructions. The syntactic and semantic rules work in the following manner: a. First syntactic agreement rules are applied for parsing. b. If they fail, semantic constraints are checked. c. If the constraints are satisfied, semantic rules determine the parse.

Abstract

Associative Classification leverages Association Rule Mining (ARM) to train Rule-based classifiers. The classifiers are built on high quality Association Rules mined from the given dataset. Associative Classifiers are very accurate because Association Rules encapsulate all the dominant and statistically significant relationships between items in the dataset. They are also very robust as noise in the form of insignificant and low-frequency itemsets are eliminated during the mining and training stages. Moreover, the rules are easy-to-comprehend, thus making the classifier transparent. Conventional Associative Classification and Association Rule Mining (ARM) algorithms are inherently designed to work only with binary attributes, and expect any quantitative attributes to be converted to binary ones using ranges, like “Age = [25, 60]”. In order to mitigate this constraint, Fuzzy logic is used to convert quantitative attributes to fuzzy binary attributes, like “Age = Middle-aged”, so as to eliminate any loss of information arising due to sharp partitioning, especially at partition boundaries, and then generate Fuzzy Association Rules using an appropriate Fuzzy ARM algorithm. These Fuzzy Association Rules can then be used to train a Fuzzy Associative Classifier.

Abstract

Large scale reconstructions of camera matrices and point clouds have been created using structure from motion from community photo collections. Such a dataset is rich in information; we can interpret it as a sampling of the geometry and appearance of the underlying space. In this dissertation, we encode the visibility information between and among points and cameras as visibility probabilities. The condi- tional visibility probability of a set of points on a point (or a set of cameras on a camera) can be used to select points (or cameras) based on their dependence or independence. We use it to efficiently solve the problems of image localization and feature triangulation. We show how the conditional probability can be combined with other measures to prioritize a set of points (or cameras) for matching and use it for fast guided search of points for the image localization problem. We define the problem of feature triangulation as the estimation of 3D coordinate of a given 2D feature using the SfM data. Our approach can guide the search to quickly identify a subset of cameras in which the feature is visible. Other than image localization and feature triangulation, bundle adjustment is a key component of the reconstruction pipeline and often its slowest and the most computational resource intensive. It hasn’t been parallelized effectively so far. We also a present a hybrid implementation of sparse bundle adjustment on the GPU using CUDA, with the CPU working in parallel. The algorithm is decomposed into smaller steps, each of which is scheduled on the GPU or the CPU. We develop efficient kernels for the steps and make use of existing libraries for several steps. Our implementation outperforms the CPU implementation significantly, achieving a speedup of 30-40 times over the standard CPU implementation for datasets with upto 500 images on an Nvidia Tesla C2050 GPU.

Abstract

Capturing the shape and texture of large structures such as monuments and statues at very high resolution is extremely expensive, both in terms of time as well as storage space. In many cases the inner details are generated by surface properties of the material, and the appearance is statistically uniform. In this paper, we present an approach to add surface details to a coarse 3D model of an object based on two additional information: a set of images of the object and a high resolution model of the material that the object is made of. The material model that we employ is the Polynomial Texture Map (PTM), which captures the appearance of a surface under various illumination conditions. We use the observed images of the object as constraints to synthesize texture samples for each triangle of the object under any given illumination. The primary challenge is to synthesize a polynomial model of the texture, where the constraints arise in the image domain. We use the knowledge of object illumination to map the texture models into image space and compute the opti- mal patch. The texture transfer then happens as a complete 3D texturemodel. We also consider the problems of pose, scale, reflectance and smoothness of surface while carrying out the texture transfer. We synthesize the texture of an object at a per-triangle basis while carrying out operations such as normalization and blending to take care of discontinuities at the edges.

Abstract

Biometrics has a long history, and is possibly as old as the human race itself. It is often used as an advanced security measure to safeguard important artifacts, buildings, information, etc. Biometrics is increasingly being used for secure authentication of individuals and is making its presence felt in our lives. With the fast increase in computational power, such systems can now be deployed on a very large scale. However, efficiency in large scale biometric matching is still a concern as the problem of deduplication (removal of duplicates) of a biometric database with N entries is O(N2), which can be extremely challenging for large databases. To make this problem tractable, many indexing methods have been proposed that would speed up the comparison process. However, the expectation of accuracy in such systems combined with the nature of biometric data makes the problem a very challenging one. Biometric identification often involves explicit comparison of a probe template against each template stored in a database. An effective approach to speed up the process is that of filtering, where a lightweight comparison is used to reduce the database to smaller set of candidates for explicit comparison. However, most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this work, we show that a cascade of simple linear projections on random lines can achieve significant levels of filtering. Each stage of filtering consists of projecting the probe onto a specific line and removal of database samples outside a window around the probe. The approach provides a way of automatic generation of filters and avoids the need of developing specific features for different biometric traits. The method also provides us with a variety of parameters such as the projection lines, the number and order of projections, and the window sizes to customize the filtering process to a specific application. The experiments are performed on the fingerprints, palmprints and iris. For both iris and palmprint datasets, the representation that we use (before projection) is the popularly used thresholded filter response from pre-defined regions of the image. Experimental results show that using an ensemble of projections reduce the search space by 60% without increasing the false negative identification rate in palmprint. However for stronger biometrics such as iris, the approach does not yield similar results. We further explore this problem to find a solution, specifically for the case of fingerprints. The fundamental approach here is to explore the effectiveness of weak features in a cascade for filtering fingerprint databases. We start with a set of potential indexing features computed from minutiae triplets and minutiae quadruplets. We show that by using a set of random lines and the proposed fitness function, one can achieve better results that optimized projection methods such as PCA or LDA. Exvi vii perimental results on fingerprint datasets show that using an ensemble of projections we can reduce the penetration to 26% at a hit rate of 99%. As each stage of the cascade is extremely fast, and filtering is progressive along the cascade, one can terminate the cascade at any point to achieve the desired performance. One can also combine this method with other indexing methods to improve the overall accuracy and speed. We present detailed experimental results on various aspects of the process on the FVC 2002 dataset. The proposed approach is scalable to large datasets due to the use of random linear projections and direcly lends to pipelined processing. The method also allows the use of multiple existing features without affecting the computation time.

Abstract

The aim of voice conversion is to transform an utterance spoken by an arbitrary (source) speaker to that of a specific (target) speaker. Text-to-speech (TTS), speech-to-speech translation, mimicry generation and human-machine interaction systems are among the numerous applications which can be greatly benefited by having a voice conversion module. Generally voice conversion systems require parallel data between source and target speakers. Parallel data is a set of utterances recorded by both source and target speakers. By having such data, one can build a mapping function at frame level to transform characteristics of source speaker to a specified target speaker using machine learning techniques (GMMs, ANNs, etc,.). These techniques are assumed to perform well as humans typically perceive transformed speech to sound more like the target speaker than the source speaker. But having parallel data is not always feasible, especially in cross-lingual voice conversion where the language of source and target speakers is dierent. In literature, voice conversion techniques have been proposed which do not require parallel data. But they require speech data apriori from source speaker. These techniques cannot be applied when an arbitrary source speaker wants to transform his/her voice to a target speaker without any apriori recording. In this dissertation, we propose a method to perform voice conversion without the need of training data from the source speaker. It alleviates the need for any speech data from source speaker apriori, and can be used for cross-lingual voice conversion system. In this method, we capture speaker-specific characteristics of target speaker. The problem of capturing speaker-specific characteristics can be viewed as modelling a noisy-channel model. The idea behind modelling a noisy-channel is as follows. Suppose, C is a canonical form of a speech signal (a generic and speaker-independent representation of the message in speech signal) passes through the speech production system of a target speaker to produce a surface form S . This surface form S carries the message as well as the identity of the speaker. One can interpret S as the output of a noisy-channel, for the input C. Here, the noisy-channel is the speech production system of the target speaker. We used an artificial neural network (ANN) to model the speech production system of a target speaker, which captures the essential speaker-characteristics of the target speaker. The choice of representation of C and S of a speech signal plays an important role in this method. We used articulatory features (AFs), which represents the characteristics of speech production process, as canonical form or speaker-independent representation of speech signal as they assumed to be speaker independent. But our analysis showed that AFs contain significant amount of speaker information in their trajectories. Thus, we propose suitable techniques to normalize the speaker-specific information in AF trajectories and the resultant AFs are used for voice conversion. We show that the proposed method could be used to alleviate the need for source speaker data, and in cross-lingual voice conversion. Subjective and objective evaluations reveal that the quality of the transformed speech using the proposed approach is intelligible and possess the characteristics of the target speaker. A set of transformed utterances corresponding to results discussed in this work are available for listening at http://researchweb.iiit.ac.in/~bajibabu.b/vc_evaluation.html

Abstract

In utility computing, users access services which are delivered in a manner similar to metered traditional utilities such as water, gas and electricity. This model is advantageous as it does not involve the initial cost to acquire computing resources because computation, storage, network and other services are available as metered services which can be provisioned as per the customers’ demands. These services can be broadly classified as: Infrastructure-as-a-Service (IaaS), Platformasa-Service (PaaS) and Software-as-a-Service (SaaS). We are thus approaching a model where everything shall be offered as a service - XaaS. These modern data- centers and clouds are distinguished by a utility pricing model where customers are charged based on their utilization of computational resources, storage and transfer of data. With the recent emergence of cloud computing based services on the Internet, MapReduce has emerged as the paradigm of choice for developing large scale data intensive applications which are distributed in nature. MapReduce works best for embarrassingly parallel workloads, in which little or no effort is required to separate the problem into a number of parallel tasks which can run independently on a cluster of machines. This is often the case when a bigger problem can be divided in a number of smaller problems that can run in parallel and there exists no dependency between those parallel tasks. MapReduce is used by more than a 100 organizations worldwide to perform tasks such as web crawling and indexing, social media monitoring, scientific data processing, data mining and machine learning. The Apache Hadoop framework is the leading open source implementation of the MapReduce model. It provides a distributed file system called HDFS or Hadoop Distributed File System that facilitates high throughput access to application data. The data stored on HDFS is divided into smaller chunks of configurable size anddistributed across the cluster. HDFS creates multiple replicas of data to ensure data availability at all times. Hadoop follows a rack aware data placement policy and ensures data availability under situations ranging from a single node failure to a complete rack disconnect. Hadoop is designed to work on cheap commodity hardware, is scalable and resilient against machine failures and works on clusters comprising a single node to thousands of nodes. It is also adapted by a number of educational institutes for performing research where the budgets are even tighter. The cost to support such an infrastructure is an important factor for consideration while setting up the cluster. Power consumption of datacenters has become a key factor contributing to the costs incurred by a service provider. This power related cost includes investment, operating expenses, cooling costs and environmental impacts. Also given the scale at which these applications are deployed, minimizing power consumption of these clusters can significantly cut down operational costs and reduce their carbon footprint - thereby increasing the utility from a provider’s point of view. For High Performance Computing systems, where the main focus is on improving the performance at any cost, these energy related costs have increased significantly to a point where they are able to surpass the actual hardware acquisition costs. The first problem that we address in this thesis is: Energy conservation for clusters of nodes that run MapReduce jobs. This problem becomes more important as there is no separate power controller in MapReduce frameworks such as Hadoop. We attempt to reduce the energy consumption of datacenters that run MapReduce jobs by reconfiguring the cluster. We propose an algorithm that dynamically re- configures the cluster based on the current workload and turns cluster nodes on or off when the average cluster utilization rises above or falls below administrator specified thresholds, respectively. Our implementation creates a channel between the cluster’s power controller module and the underlying distributed file system to dynamically scale the number of nodes and adapt to the current service demands on the cluster. We evaluate our algorithm on a variety of workloads and our results show that the proposed algorithm achieves substantial energy conservation, as compared to the default HDFS implementation. In our model, the amount of energy saved is proportional to the number of deactivated nodes. We implement the defaultrack aware replica placement policy followed by HDFS and incorporate the cluster reconfiguration decisions suggested by our algorithm to dynamically scale the number of nodes in the cluster. We demonstrate the scale up and scale down operations of our algorithm and their corresponding energy savings and observe that the cluster intelligently reconfigures itself based on the workload imposed, proving the effectiveness of our algorithm. As expected, the energy conserved in case of low workload

Abstract

A large portion of the data that resides in Enterprises, exists in the form of unstructured textual data. Unstructured data implies collection of natural language text in the form of word documents, HTML pages, plain text files, among others. Structured data on the other hand refers to relational databases, XML documents, etc; where there is a well articulated scheme to represent and store the data. Unstructured data is easy to produce and comprehend by humans, where as it is not trivial for the machines and electronic applications to extract information from it. As unstructured data lacks structure and patterns therefore, in order to target its specific portions for retrieving particular information, full text search has to be carried out. In between Internet and personal machines, we have the Intranets and data archives of various Enterprise establishments, which have their own set of guidelines for search. A typical enterprise search system would be benefited with the thoroughness of desktop search but would not want to suffer from the inherent slowness of typical on the fly, linear scans used in desktop search. An Internet search approach on the other hand may be too generic to produce precise results even though it would be advantageous for speedy indexing and retrieval. The difference and the uniqueness in the composition of enterprise data makes Enterprise search an entirely different beast. This thesis concentrates on enhancing the relevance of results in query based document retrieval in Enterprise Search (ES). Approaches to implement effective ES are different from standard document retrieval and web search. A number of such past and on-going works in ES research area are discussed in this thesis. Our approaches to ES work with two important open research areas in ES, namely, user context aware ES and resolving the issues of vocabulary bias and narrowness in ES. In user context aware ES, we concentrate on user context aware computerized work environment, to facilitate better understanding of user’s information requirements in ES systems. Issues of vocabulary bias and narrowness arise when there is a difference between vocabulary used for queries and that used in the content (though meaning could be the same). In any form of document retrieval, the result-set is strongly query dependent. As a query represents the information need of a user, a misrepresenting or an ambiguously formed query dilutes the result quality. We try to reduce the difference between the vocabulary used in constructing the query and that used in enterprise content for resolving this issue. We enhance ES by concentrating at two basic aspects of information retrieval, which are query expansion and re-ranking. User and work context aware ES is implemented by through a user role based query expansion using local & global analysis techniques and re-ranking result-set by a role based document classification. External lexicon and thesaurus repositories constructed from open source on-line encyclopedias such as Wikipedia are used as complementary knowledge bases for implementing the query expansion for countering the vocabulary bias issues in the enterprise. This is accomplished by extracting and using a Wikipedia concept thesaurus from the article link structure of Wikipeida for query expansion. The results are re-ranked step using large manually tagged sets rich in categories from all domains of Wikipedia. These tagged sets are used to train a classifies which classifies search results into various enterprise document classes. Documents belonging to the dominant classes in the result-sets are given higher preference in re-ranking. We also introduce broader vocabulary range into result-set with collection enrichment techniques usingWikipedia article text for pseudo relevance feedback. Both supervised and unsupervised classification techniques are used to determine good feedback documents from the pseudo-relevant set. We evaluate our approahes on two datasets, namely the IIIT-H corpus and the CERC dataset. Role based personalization in ES required a dataset with role based topics and relevance judgments. As no such dataset existed, we customized the IIIT-H intranet data for the same. Rest of the techniques were evaluated on standard ES evaluation platform, i.e. the CERC dataset provided by the TREC Enterprise track. Role based personalization shows improvements for both local and global analysis based query expansion technique as well as for the re- ranking compared to plain test retrieval. Wikipedia concept thesaurus based query expansion reveals gains in recall figures, without diluting the precision in search results. Wikipedia category network based re-ranking method shows moderate improvements in the precision figures for the results. Improvements are also observed in Wikipedia article text based pseudo relevance feedback in comparison to blind relevance feedback without enriching the pseudo relevant

Abstract

The problem of Secure Message Transmission (SMT) has been widely studied in the field of dis- tributed computing. The interplay between network connectivity and possibility/feasibility of SMT has been one of the most interesting topics in this area and has been studied by researchers for over three decades. The two main questions that are addressed in these studies are: Given a network which is not fully connected and contains some faulty nodes, (i) Is SMT possible in the network? and (ii) Is effi- cient SMT possible in the network? These questions have been answered for various network and fault models and for several variants of SMT. In our work, we study the case where the network is directed and the fault is active or Byzantine. In this model, we answer the first question for two variants of SMT, viz., (1, δ)-SMT and (ǫ, δ)-SMT. It has been a popular practice to model the network as a set of channels or wires between the sender node S and the receiver node R. Such a model treats all the intermediate nodes as simple message- forwarding routers and assumes that no local computations are required of them to enable SMT from S to R. But recent results have shown that, unlike in the case of undirected networks, such a modelling in directed networks can completely ignore nodes that play a crucial role in determining the possibility of SMT from S to R. Moreover, to actually affect the possibility of SMT in the network, these nodes must be capable of doing local computations. In other words, there exist directed networks in which SMT from a node S to a node R is possible only if the intermediate nodes (or routers) are given the power to compute locally. In this thesis, we focus on networks of this kind, i.e. directed networks with computational routers and find the minimal connectivity required in the network for the possibility of (1, δ)-SMT and (ǫ, δ)-SMT.

Abstract

The constant development of medical imaging technology and the dependence of medical professionals on medical images as an auxiliary data continues to increase the demand for more advanced and practical solutions that can be used to analyse the information in these images. Due to the wide variety of information present, its subtle nature, and the need for highest accuracy, analysis of medical images provide an excellent test case to many image analysis algorithms. Computer aided diagnostic (CAD) systems are developed considering these factors. Often, CAD systems comprise of multiple algorithms concatenated in a pipeline to perform different operations. The final output is aimed to be interpretable by a medical professional. In this thesis, three image processing techniques - i) enhancement, ii) segmentation and iii) shape analysis are studied. A CAD system is built using these three operations forming different blocks of pipeline. The intent of this system is to aid in diagnosis of a specific variant of dementia known as Alzheimer’s disease (AD) by detecting the stage of an object of interest, a sub-cortical structure called hippocampus. AD is a neurodegenerative disease with crippling effect on human mind and yet, there is no known cure to this disease. Hence, the detection of its early onset proves to be helpful to monitor and regulate its progress. Quantitative analysis performed on segmented volume of hippocampus has been used to classify it into one of the three stages of diagnosis - normal, mildly affected by the disease and severely affected by the disease. In order to study the structural changes in sub-hippocampal regions with respect to progression of the disease, the piecewise quantitative information has been used. Segmentation of hippocampus is performed by a physics-based model that operates on local and global image forces to evolve a surface. A coarse segmentation method is used to obtain an estimate of the surface to be used in evolution. Since the segmentation method used here is dependent on intensity-based features, image contrast enhancement is performed by an adaptive intensity windowing algorithm which forms the preprocessing stage to segmentation. Thus, each block operates on the output of the previous block, and the output of the final block is useful information to diagnosis. The evaluation of intensity windowing is done by comparing the quantitative measures of accuracy of segmentation algorithm on non-windowed and windowed images from one of the publicly available datasets. The evaluation of segmentation is done by conducting experiments on images from two publicly available datasets and by comparing its accuracy with those from other recent methods on the same datasets. Also, to demonstrate its general applicability, three other structures have been segmented using this segmentation method. Images and their corresponding diagnostic information, belonging to three different stages, have been used from another publicly available dataset for quantitative analysis of hippocampus. The segmentation is performed by the proposed method and its output is used for classification into different stages. Encouraging results have been observed at each of these stages. That is, the proposed method does not perform exceedingly better than some methods, but does so than some others. With further investigation to address the pitfall of the proposed method, one could expect to achieve results to match the state of the art, retaining the advantages of this method. The proposed segmentation method has been tested on 43 volumes drawn from two publicly available datasets and by measuring Dice coefficient with reference to hand segmented ground truth, hippocampus was segmented with an average accuracy of 77%, amygdala with an average accuracy of 68%, caudate with an average accuracy of 82% and putamen with an average accuracy of 80%. The classification scheme has been assessed on 150 volumes from the ADNI database and the normal hippocampus was differentiated from the abnormal cases with an overall accuracy of roughly 80%.