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%.

Abstract

The past decade has seen tremendous advancements in consumer electronics and web technology. The proliferation of digital cameras and popularity of content sharing sites have caused a rapid growth in creation and distribution of images and videos by home-users. Enhancement and manipulation of captured images is fairly popular among common users due to availability of numerous easy-to-use photo editing utilities like Instagram, Picasa, Photo Gallery, etc. In comparison, video manipulation is still less popular among common users due to the lack of easy-to-use yet powerful video editing platforms. Basic video editing platforms for home-users are simple and intuitive, but these tools provide limited functionality such as split and merge videos, add captions or audio etc. Professional video editing platforms are rich in functionality, but these tools demand high technical expertise for use. A novice user usually gets discouraged by complex interactions and cumbersome processing. Moreover, the traditional video editing interfaces model and represent videos as a collection of frames against a timeline. In a user’s perception, a video has more meaningful semantics such as objects, actions, events, interactions, etc. The gap between perception and representation makes object-centric manipulation of videos an unnatural and laborious task. In this thesis, we attempt to bridge the gap between the power and usability of video manipulation interfaces by using computer vision techniques. We propose a representation based on three high-level video semantics, scene mosaic, object motion, and camera motion to enable simple and meaningful interaction for object-centric navigation and manipulation of long shot videos. We build an extended field of view mosaic of the video scene and represent object motion in this scene mosaic using 3D space-time trajectories. We define novel object and camera manipulation operations using object trajectories as basic interaction elements. The use of object trajectories as basic video semantics replaces complex interface elements by interactive curve manipulation operations. The object operations allow the users to perform various temporal manipulations on the video objects by interactively manipulating the object trajectories. For example, users can delay or advance the video objects by dragging the trajectories along the timeline or replicate objects by creating multiple copies of the object trajectories. The camera operations model the camera as a movable and scalable aperture and allow the users to simulate camera pan, tilt, and zoom by creating new aperture trajectories. Object and camera operations, in combination allow users to perform a number of high-level video manipulations in a simple ‘click and drag’ fashion.

Abstract

The Business enterprises today are rapidly seeking ways to move towards automation of their business processes. With the fierce competition in the market, the enterprises are constantly looking to evaluate their business processes and reduce the operational costs. With the inclusion of information technology in all spheres of life now, much of this work is (semi) automated and is done through work-flows. Workflows are a collection of tasks organized to fulfill a business process goal. Each of these tasks are performed by a human or some computer application. The software systems that help define/specify the workflows and execute them are known as workflow management systems. Current workflow management systems need to be rewritten/extended to support complex business processes. With technology getting advanced and business processes getting complex by the day, it is likely that business enterprises will desire to change the specification and execution procedures for user workflows to ensure most tech- nological advanced and improved methods of specifying and executing their workflows. But, most of the workflow management systems specify or execute workflows according to a fixed set of rules coded in the workflow engine and do not provide for dynamic change in workflow specification and execution procedures.

Abstract

In this era of interconnected digital information society, increased risk of private data theft has led to the deployment of enhanced levels of security in the form of Biometric Authentication. Fingerprint based authentication is a reliable, well-established, trusted and continually employed trait of biometrics. In the recent past, Automated Fingerprint Identification Systems (AFIS) have been deployed in numerous commercial and civil applications for stronger authentication. In majority of these applications, the fingerprint capture mode is unsupervised and hence, the conditions in which the fingerprint is captured can deviate from ideal conditions. The quality of the digital version of fingerprint obtained is low with increased frequency. Various kinds of fingerprint noises are induced due to improper pressure, bad skin conditions, sensor noise and instantaneous capture of fingerprint scanner during fingerprint acquisition. Objective of a Fingerprint Enhancement algorithm is to restore the degraded, vandalized or smudged regions of fingerprint from the context and mask the regions where the contextual information does not serve as a reliable prior. Traditional enhancement is based on smoothening the ridges of the fingerprint image in their corresponding local orientation. Various models fail to incorporate the global smooth ridge and valley structure in the fingerprints. In this thesis, we propose a novel and robust approach that considers the fingerprint enhancement as a global fingerprint model restoration problem and model the Hierarchical Markov random fields to attribute to the ridge continuity in the coarser level and local orientation smoothness at a finer level. We attempt to deal with the problems faced by the traditional enhancement models during the noise elimination in the fingerprints. Issues we try to address include removing the artifacts and spurious structures introduced by the traditional filters in the enhancement stage of noisy fingerprints due to their basic assumption of fingerprint signal as a sinusoidal pattern and recovering the regions that contain sufficient neighbour information or finer level information to provide more information from the noisy fingerprints through our inference schema. We train our MRF with real world fingerprint patterns. The global ridge continuity modeling from local neighbourhood ensures that spurious structures in noisy fingerprints are suppressed to provide more reliable and genuine information which is alarmingly scarce in noisy fingerprints. Further, we propose a hybrid fingerprint enhancement algorithm that utilizes the parallel computation power of GPU. We divide the fingerprint into blocks and label them from good quality to very noisy regions and attempt to recover the noisy regions from the information provided by the good quality regions and the MRF inference from the neighbourhood regions. Finer level orientation information is vi vii used to index the candidate patterns for a region and belief propagation framework is used to infer the fingerprint structure. This model runs at higher speeds and switches from using the local parameters to global parameters wherever required. We perform our experiments on the highly noisy fingerprints obtained from the FVC databases and also a software called SFINGE which generates artificial fingerprints with controlled parameters. We adjust various parameters of noise to model highly noisy fingerprints. Parameters such as sensor noise, scratches, creases, noise due to improper contact etc. are varied from medium to high to generate various models of noisy fingerprints in various classes. Algorithms that restore the fingerprints at high noise levels are required to facilitate the robustness of an AFIS since noisy fingerprints contribute to more than 10% of the database. In this thesis, we try to address this issue and also try to reduce the gap between manual and automated fingerprint recognition in noisy images. The experimental results for various public and synthetic databases prove the efficiency of our approach.

Abstract

The semiconductor industry has witnessed vast improvements in the Integrated Circuits (IC) manufacturing process. Using modern fabrication process a single IC can accommodate billions of transistors in the same area on which there used to be few thousands of transistors not too far ago. However, as is the case with every other area of science, with the sophistication of the technology there is increased complexity involved in the manufacturing process of an IC. The modern IC manufacturing process contains hundreds of steps and there are bound to have errors in several if not all of the steps involved. These defects in manufacturing process translate into variations in the device parameters which in turn affect the performance of the circuits. This thesis addresses the issue of process variations and its effects on one such circuit namely Static Random Access Memory (SRAM). SRAMs are used extensively in cache memories in personal computers, multimedia computers, networking applications etc. SRAMs are known to be vulnerable against process variations and with the continuous scaling of the device sizes, the percentage of cells that do not have acceptable read and write characteristics is increasing. Therefore it is felt that there is a need to have an idea of the cells which do not meet the required characteristics because of process variations. In view of this, investigations have been carried out in this thesis on the read time and write time of SRAM cells with process variations. In the first instance, it is thought that the width, length, oxide thickness and threshold voltage of the transistors are the parameters that vary the most from device to device. Two possible models for these variations are assumed – The first one is Global variations i.e. all the device parameters in a cell vary almost by the same percentage and those variations are random from cell to cell. The second model (local variations) assumes that parameters of each device in a cell are affected independent of parameters of other devices. So the variation here is with respect to device to device. The effects of these variations on read and write time of SRAM cell has been evaluated through the simulations of SRAM cell using HSpice tool. The nominal device parameters are the ones corresponding to BSIM parameters of 65nm technology. It has been found that the variations in threshold voltage produces maximum variation in read time and write time of SRAM cells when only one of the parameters is varied keeping the other parameters constant. Nearly 20% of the cells have been found to be outside the range of +/- 10% of the median value. The effect of W, L and Tox does not seem to affect much pronounce if they are considered separately. When all the parameters are assumed to be varying simultaneously, nearly 25% of the cells have been found to be outside the tolerable limit. From the above it may be seen that the number of cells which have largely varying read time and write time due to process variations is quite high. Therefore, it is thought necessary to think of a simple method of reducing the number of unusable cells. Experiments have been conducted by varying address signal characteristics namely rise time. It has been found that with increase in rise time from 2ps to 0.2ns (200ps), it is found that the number of cells in which the writing and reading could be successfully carried out have gone up. The number of cells in which the writing and/or reading could not be carried out with address signals having 200ps rise time have dramatically reduced to 1.8% and 2% from 18% and 10% respectively. In doing so, however, the speed of the SRAM cell is found to be lower for higher rise time values of the address signal. Hence, for lower rise time, it is shown that the speed is good but the yield is low and for higher rise time, the speed is low but the yield is high. It is therefore thought necessary to have tradeoff between the yield and speed of operation and has been explained in the thesis. Thus the thesis first investigates the effect of process variations on the read ability and the write ability of SRAM. Thereafter it has come out with a simple technique of improving this feature of SRAM cells by modifying the characteristic of the address signal.

Abstract

In recent years, graph theory is being extensively used to study large scale and complex systems from diverse disciplines, viz. physical, biological, computer and social sciences. Various models viz. scale-free, small-world, etc. apart from random graph and regular graphs have been proposed. In this thesis, we present a detailed analysis of topological and structural properties of two air transportation networks, airport network of India (ANI) and world airport network (WAN) using graph theoretic approach. These air transportation networks have been constructed by considering airports as nodes, direct flight routes between them as edges and number of flights on each route as the weights. The heterogeneity in connectivity and long-range couplings observed in these networks suggest that certain nodes may play an important role in maintaining the stability and efficient flow through the network. Identification and analyzing the impact of targeted removal of such “critical” nodes on the network is the major focus of this thesis. This has been carried out by analyzing various graph centrality measures, viz., degree, strength, betweenness and closeness in the context of air-traffic flow. Such an analysis would not only enable us to improve the infrastructure and air connectivity and help in promoting tourism, but also help in identifying crucial airports and routes to regulate traffic in emergencies such as accidental failure of an airport, diverting traffic to avoid congestion and delays during unexpected climatic changes, etc. In the last few decades, we have observed the main cause of the epidemic turning into pandemic of an infectious disease is its transmission over the densely connected air transportation services. Using a simple SIR (Susceptible-Infected-Recovered) compartmental model for disease spread, our analysis of graph centrality measures suggests that by reducing flights on important routes, the spread of the disease can be curtailed. We observe that though these air transport networks exhibit small-world and scale free behaviour, the preferential growth Barabasi-Albert (BA) scale free model fails to explain the growth and certain topological properties of these networks. We carried out a comparative study of various scale-free models with the actual airport networks and propose a model which captures the evolution of these transportation networks.

Abstract

Hierarchical Ring Networks constitute an interesting class of networks, from the point of view of their range of applications and their design considered as a graph theoretic problem. They are widely used in logistics, communications sytems and the design of shared multiprocessors. In logistics, the distribution network comprising of manufacturing centres, warehouses and retail outlets can be modeled as Hierarchical Ring Networks (HRNs). In the context of processor design, shared memory multiprocessors face the problem of scaling due to the fact that the CPU-to-memory connection becomes a bottleneck, with multiple processors vying for the available RAM. The inherent structure of HRNs has been shown in literature to help scale these systems. In communications systems, they can be used to have equitable distribution of load in flat architectures that are prevalent in new generation communication systems. It is imperative that efficient design of these structures has to be carried out, to reduce costs of their installation and help increase their ease of management. In this thesis, we look at methods to design low cost HRNs. This np-hard problem is stated in the context of Euclideancosts of the links and the cost of the capacity to be installed on these links to help put up these networks. Extensive work has been done in literature for the design of 2-level HRNs only. Further, most approaches to a solution deal with the problem in a series of chronological steps, which will end up giving us sub-optimal solutions. We first present methods to design 2-level HRNs. Following this, we solve the generic, k-level HRN design problem. We provide formulations of the problems and present heuristics based on Simulated Annealing and Genetic Algorithm. Our algorithms provide a holistic approach to the problem at hand and we have used them to design networks containing upto 1000 nodes, where previous methods only solved for nearly 120 nodes. Our results show that our algorithms consistently perform better than CPLEX – which we have used for comparison. We then proceed to show one important application of HRNs in 4th generation Radio Access Networks. We try to understand the performance of a topology based on HRNs in the Evolved Universal Terrestrial Radio Access Network or EUTRAN – the radio access network of LTE systems.

Abstract

Domain Name System(DNS) is an inevitable component of the critical infrastructure of the Internet. It is a hierarchical distributed database system which provides a crucial service for the internet i.e. the mapping of human-friendly domain names to their respective machine-friendly IP addresses and vice versa. Almost all internet-based applications including http, ftp and email, need to resolve a given domain name to its respective IP address prior to establishing connections. DNS provides the mapping service which is fundamental not only to the health of the Internet but also to the protection and integrity of the data. In case, mapping of a domain name to an IP address in the system gets corrupted, the system would no longer be acceptable. Being probably the most valuable infrastructure in the Internet, its security is of utmost priority. The domain names in a DNS database are stored in the form a hierarchical tree structure which is known as domain name space. Each node in the tree contains zero or more resource records which hold information associated with a domain name. DNS cache these resource records for a specific time period, i.e., TTL (Time-To-Live). TTL too plays an important role in maintaining the consistency of the cached resource records. Short TTL reduces the likelihood of getting old information but increases the DNS utilization whereas long TTL decreases the DNS utilization but at the same time increases the chance of retrieving outdated information. DNS was not earlier designed to save itself against different kinds of attacks such as cache poisoning, rebinding attacks etc. This is the reason that DNS servers have been manipulated by attackers to launch phishing attacks, to commit click-frauds and to drive traffic to malicious websites. Among all the different kinds of attacks on DNS, DNS cache poisoning is the prominent one. DNS cache poisoning refers to the cases where the cache of a DNS server gets corrupted due to the injection of false mapping in the server which affects the accuracy of DNS lookups. Consequently, when queries arrive at the DNS server, inaccurate and probably malicious replies are sent as the response. False mapping can be injected into a DNS in many ways i.e. by dns spoofing, dns forgery etc. After one poisoned record is injected into the cache, it can spread to other parts of the cache or other servers through query/response between servers. DNS cache poisoning could be used by an attacker to redirect the querier to a non-existent IP address, thus causing Denial-of Service or the querier can be redirected to a malicious website which drops Malware/Spyware or s/he could even be redirected to attacker’s website, causing phishing attack. Till date, many different solutions have been proposed to overcome the problem of cache poisoning but none has been deployed successfully. Certain proposed solutions like DNSSEC, DNSCurve etc, found to be efficient against cache poisoning but they have not been successfully deployed primarily because of the complexity involved in key management. Certain other solutions were neglected because either they required changes in the DNS protocol or they introduced considerable latency in the system making them an undesirable solution. Hence, in spite of all the solutions proposed till date to mitigate cache poisoning attacks, the problem still persists. To mitigate cache poisoning attacks, we have proposed an approach - Domain ConsistencyManagement system (DCMS) which makes the use of response delays for a specific resource-record type between a DNS client and a DNS server. Our approach is similar to stimulus-response model in which a response is expected for a specific stimulus within a specific period of time. If the response is received out of the period then it could be the result of some flaw in the system. Similarly in our approach we expect the response of a DNS query for a specific resource-record type to be received within a certain period of time. In case the DNS response is received out of period we suspect an attack on the system and perform a check to ensure the consistency of the response received. The significant feature of our approach lies in its self-learning that in parallel it updates its database of response delays for a specific resource- record type between specific DNS client and DNS server, thereby helping the system not only in proper functioning, even it enhances its performance. DCMS makes cache poisoning attacks, almost infeasible, even for motivated and powerful attackers. The biggest advantage of our approach is that it does not require any change in the DNS protocol, hence it could be deployed on a large scale within a short period of time. It does not even require any changes at the server side as inWSEC-DNS to ensure the consistency of the responses. Since it does not involve any cryptography technique, hence any sort of key management is not re