Abstract

User interface (UI) forms play a key role in information systems. The design of UI form is an important activity of an information system development. The improper UI increases navigational burden, increase data-entry errors, reduces the efficiency of the user, and increases the cost of both data-entry and data maintenance. Normally, user expects an effective, easy-to-learn and easy-to-operate UI. Currently, context-based framework is well-known in designing UIs. A context covers a particular functionality or task for entering data into one or more relational tables of an information system. Developing an efficient UI using context-based approach is a challenge, as the behavior of a system is known based on the contextual factors (such as the background knowledge of a user, the specific goals and objectives of a user, the work context etc). Research efforts are going on to investigate the design of efficient UIs under context-based framework. In context-based framework, the input attributes are divided into several contexts based on the functionality or task. The UI form is designed for each context. Normally, it is assumed that the system receives data values on all days for all the attributes in the context. It can be observed that, in some information systems, some attributes receive values only during certain time duration. Therefore, the attribute may be not required in the UI for the remaining duration. As a result, there is an opportunity to reduce UI size. We call such property of the attribute as “temporality” of attribute. So, for several information systems, there is a scope to exploit the temporality of attributes for the efficient UI design. As a part of this work, we have proposed improved UI design approaches both for desktop and small screens. * To develop an efficient UI design for desktop environment, the notion of attribute temporality is used to divide the large UI into several small UIs in desktop or PC-based systems. The temporality values can be fixed by analyzing the system functionality or task. By taking attributes and temporality values of a given context, we have proposed an approach to cluster the attributes into several active-contexts where each active-context can be considered as an independent UI for certain time period. We have designed UIs for a real system eSagu® (eSagu® is an IT-based personalized agro-advisory system) with this approach. The implementation results show that it is possible to improve the navigational burden significantly with the proposed approach as compared to the existing approaches. * We proposed an approach for small screens by exploiting attribute temporality to interface tailoring approach. In small screen environments, such as mobile phones and personal digital assistants, interface tailoring approaches are employed to develop UI forms. In these approaches, the PC-based UI is divided into multiple screens (or high-level components) in such a way that the UI can be accommodated in one or few mobile screens. In the proposed approach, the notion of attribute “temporality” is extended to divide a given context into several small active-contexts. However, we observed that certain active-contexts have more attributes which cannot fit in small screen. In such situations, we explored interface tailoring approaches in building suitable interfaces for mobile devices. We have carried out analysis by considering real world dataset of eSagu® system and found out that the proposed approach has a potential to improve the performance.

Abstract

Illiteracy is an impediment to India’s development as approximately only 66% of the population is literate, with about 150 million adult illiterates. Tata Consultancy Services (TCS), an Indian Software House has initiated Adult Literacy Programme (ALP) as a solution to adult illiteracy problem in India, developed ALP products under this initiative and has made over 1,20,000 people literate across India. An ALP product is an eLearning system that employs Computer Based Functional Literacy (CBFL) as the underlying technology to teach adult illiterates of a particular language. However, the goal of ALP initiative is to develop and maintain ALP products for 22 Indian languages and their major dialects. The scope also includes development of ALP products to help migrants from one linguistic region to another. It is labor intensive to develop and maintain a family of ALP products because of the enormous number and huge variety of products. The ALP Problem is concerned with the automating the development of a family of ALP products. In this thesis, we present ALP Factory as a solution to the ALP Problem, which has resulted significant improvements in productivity levels of up to 89%. We have also developed Prerak Training Factory that pertains to automating the development of training software for the ALP family of products. We found that development of a family of eLearning Systems is a common problem in developing computer-based systems for computer literacy, technical literacy, vocational training, school education and thus presenting us with the problem of automating software development. Despite many decades of intensive research towards software industrialization and software product lines to improve software productivity, the automation problem is a daunting task. In this thesis, we propose TALES as an approach towards automating the development of a family of eLearning Systems. This approach was borne out of our experience of providing a solution to the ALP problem and inspired from traditional manufacturing. This approach progresses by first gaining a deeper understanding of the problem domain, designing a solution based on this understanding to handle the scale and variety of software products. Then various kinds of standardization focusing on product structure, production processes, and raw material are developed to provide a standard platform that enables assembly and interchange of parts. The next step involves the development of Virtual Assembly Lines that facilitate the automation of production processes by weaving processes with tools and hence producing a family of similar but distinct products. This approach is supported by repositories of components, tools, and processes. The uniqueness of TALES approach is its ability to facilitate non-technical users during software development.

Abstract

An accurate and up-to-date road network database is essential for GIS (Geographic Information System) based applications such as urban and rural planning, transportation management, vehicle navigation, emergency response, etc. Often, road database is generated through field surveys with the help of GPS (Global Positioning System) enabled instruments. This approach of road extraction, however, is time consuming and labour intensive. With increase in availability of satellite imagery both in high and low resolutions, automatic road network extraction from satellite imagery has received considerable attention and has been studied extensively since 1970s. Since road region appear as linear segment in low resolution images, earlier research on road extraction focused on extracting road center-line from low resolution images. On the other hand, high resolution satellite images provide an opportunity to extract entire road area, which are particularly useful for vehicle navigation, along with road network. However, factors like occlusion due to trees along the road, building shadows and vehicles, variation in road surface characteristics (example, bitumen road versus concrete road) and changing road geometry, which become more apparent in high resolution satellite images, make road extraction a challenging problem. Though several road extraction techniques have been proposed in the literature, because of various assumptions about the road region characteristics and imaging modality, such techniques often exhibit limited success in the real scenarios. Of these techniques, the region growing based road extraction approach, in which road extraction starts with road seed points (either provided manually or generated automatically) and extracts entire road region based on predefined matching criteria, holds considerable promise. In this thesis we present a novel automatic road extraction algorithm based on adaptive texture matching (ATM-R), which is a variant of region growing approach. The algorithm developed in this thesis is robust to variations in radiometric resolution of input images, road surface characteristics (texture) and road geometry and is scalable to inputs from different sensors. In the proposed algorithm, difference in a pair of closely dated multi-temporal images of same geographic area has been studied to generate road templates (seeds) and such templates are further utilized to extract remaining road region within a road template matching framework which adapts to local road texture. To assess the performance of the proposed algorithm, a set of images encompassing wide range of radiometric resolutions and different spatial resolutions were prepared. The image set consisted of grayscale equivalent of pan-sharpened IKONOS images, panchromatic CARTOSAT-2 images and grayscale equivalent of images captured from Google Earth. Images were chosen to include road regionswith varying width, wide range of road surface reflectance values and different textures. Experiments were conducted on images with disconnected road segments, multi-lane roads, road regions occluded with trees and images with road over-bridges. The measures used to evaluate the algorithm performance are road network completeness, road area completeness and correctness of road area. The algorithm is able to consistently extract 70% to 90% of the road network and has a high performance against all the three measures. Along with generating road network the proposed algorithm is able to extract road area which can be further used to assess road width, number of road lanes and other auxiliary road network information. The algorithm has considerably reduced manual intervention in road extraction process and avoided the need for GCPs (ground control points).

Abstract

Nanoparticles are the particles having dimensions in the range of 1- 100 nanometres. In recent years the synthesis and characterization of nanoparticles have gained a lot of attention due to their potential application in drug delivery system and as biosensors along with their unique optical, chemical, electronic, and mechanical properties which is missing from their bulk counterparts. The nanoparticles can be surface engineered to achieve specific goal. The ability to exploit and modify the properties of nanoparticles, makes it a potential agent for the enhancement of the performances of the enzyme-based biosensors. Iron nanoparticles are magnetic nanoparticles which make it easy to be detected and potential transducer. Biosensors are analytical devices that perform qualitative and/or quantitative estimation of analytes. An essential requirement is a biomaterial that selectively recognizes the molecule of interest. Applications of nanomaterials to biosensors have recently aroused much interest. This is because these interesting materials exhibit large surface-to-volume ratio, high surface reaction activity, high catalytic efficiency, and strong adsorption ability that are helpful for the immobilization of desired biosensing molecules. Moreover, nanoparticles have a unique ability to promote fast electron transfer between electrode and the active site of the enzyme. In this work, magnetite (Fe3O4) nanoparticles have been modified using glutathione and then proteins (BSA and HRP) have been coupled to study the characteristics of the nanoparticles for possible biosensor application. It has been found that the immobilized HRP displays excellent catalytic property to H2O2, and Fe3O4 nanoparticles in the biosensing interface offer not only friendly environment to immobilize HRP but also improve the electron transfer between analyte and glutathione modified Fe3O4 electrode surface. HRP/GT+Fe3O4 bioelectrode shows high sensitivity, low detection limit and fast response for analytes. In addition, the HRP/GT+Fe3O4 based biosensor exhibits both good reproducibility, long-term stability and cost effectiveness. It should be considered as an approach to develop biosensors for immunological and pathological analytes.

Abstract

The RoboCup Rescue Simulator, a generic urban disaster simulation environment implemented and deployed on a network of computers, provides a platform for multi-agent systems research problems such as heterogeneous agent coordination and cooperation, and decentralized decision making using incomplete information. A scoring scheme based on the concept of a Score Vector is proposed and evaluated. The ScoreVector is a vector of parameters which monitor the game and provide analysis of the game by considering various crucial aspects in detail.

Abstract

In E-commerce environment, a customer faces several difficulties while selecting the product. In this thesis we have investigated the issue of product selection and proposed an improved framework to help the customers in efficient product selection. In addition, we have studied the problem of resume selection and proposed an improved approach to help the Human Resource (HR) managers for selecting the appropriate candidate. We first discuss the contribution related to the problem of product selection. Suppose a customer wants to buy a Nokia mobile phone. To select a mobile phone, the customer has to carefully go through the features of about 70 Nokia mobile phone models, where each mobile phone description consists of around 30 features. As the different variants of mobile phones are available, the customer has to manually browse several Web pages to identify the specialty of each mobile phone. Even after spending sufficient amount of time, sometimes, a customer may not be confident of his/her choice. Customers face the similar problem in several scenarios such as selecting a laptop, television, iPod camera and so on. We have investigated the issue of selecting the appropriate product from the group of similar products and proposed an improved framework for the efficient product selection. It can be observed that every product has some specialty and possesses corresponding special features. The basic idea is as follows: a customer is shown special feature of each product along with the common features. As a result, the customer can quickly browse through all the special features and make the appropriate selection. We propose a method to compute the specialness value of features. On the basis of specialness value, we propose a clustering algorithm to organize the features into two-level and three-level organization of features. We have conducted the experiments on three real world data-sets related to Nokia-mobile phones, Sony-cameras and HP-laptops for the validation of proposed framework. The results indicate that the proposed approach has a potential to reduce the number of features customers need to browse for selecting a product and thus help customers to select an appropriate product from a set of similar products efficiently. The contribution related to the problem of resume selection is discussed as follows: Big enterprises and corporations receive thousands of resumes every day. Currently available techniques or services employed by these enterprises help their HR managers to filter from thousands of resumes to hundred potential ones. Since these filtered resumes are similar to each other, they have to manually look through each resume to select the appropriate candidate. We have investigated the problem of resume selection from set of similar resumes and proposed an efficient framework to solve the same. We have used the notion of special features as proposed above to come up with a solution to resume selection problem. It can be observed that normally resume information is described in a hierarchical structure with several sections such as education, experience, skills and achievements. There may exist special information present in some resumes when compared to others. For example resume may contain specialty in education, specialty in experience, special skills or special achievements. In this work we have experimented with extracting `special skills' from a given set of similar resumes to ease the process of resume selection. It can be observed that all the resumes have some common skills and each resume might contain some special skills when compared to others. The proposed approach identifies the special skills (if present) in each resume and organizes the skills information in an efficient manner to help the HR managers select resumes efficiently. We have performed experiments on real world data-set of resumes and obtained encouraging results. The results show that it is possible to select resume on the basis of special skills present in each resume. Overall in this thesis, we have proposed the notion of special features and shown that how the knowledge of special feature could help the users in product selection in E-commerce environment and help the HR managers to select appropriate candidate through filtering of resumes based on the skills present in each resume.

Abstract

The rapid expansion of the Internet is receiving a great deal of attention world-wide. The technological developments and the increase in online communications have played a vital role in revolutionalizing the Information Age. There is a tremendous growth of online applications to manipulate the user’s personal data, which has resulted in the widespread availability of the user’s personal data in the digital form. This raises the issue of privacy protection against potential misuse of this data by legitimate service providers or intruders. Without proper countermeasures to thwart the attacks, security problems become a major threat and a serious impediment to further development of business applications on communication systems. Many of the current solutions provides information security by assuming a level of trust among the parties. The leakage of the critical data to third parties is prevented by applying cryptographic primitives as a secure layer over the standard algorithm. On the other hand, privacy preservation computation is more closely related to Secure Multiparty Computation (SMC). SMC enables two parties; one with the function f() and the other with the input x; to compute f(x) without revealing them to each other. However, the solutions based on the general protocol of SMC requires enormous computational and communication overhead, thus limiting the practical deployment of the secure algorithms. In this dissertation, we focus on development of ‘highly-secure’, ‘comunication and computationally efficient’ algorithms to problems with ‘immediate impact’ in the domain of computer vision and related areas. Security issues in computer vision primarily originates from the storage, distribution and processing of the personal data, whereas privacy concerns with tracking down of the users activity. The primary challenge is in providing the ability to perform generic computations on the visual data, while ensuring provable security. In this thesis, we propose lightweight encryptions for visual data, such that the server should be able to carry out the computations on the encrypted data and also store the stream if required, without being able to decipher the actual contents of the image. Moreover, the protocols are designed such that the interaction and the data communication among the servers is kept to a minimum. It has been proven before that the best way to achieve secure computation on a remote server is by using the cryptographic protocol of SMC. Thus, a method that provides provable security, while allowing efficient computations without incurring either significant computation or communication overhead has remained elusive till now. We show that, for designing secure visual algorithms one can exploit certain iii iv properties such as scalability, limited range etc, inherent to visual data to break this impenetrable barrier. We study and propose secure solutions for applications such as Blind Authentication, i.e. blindly authenticating a remote-user using his biometric. Subsequently, we present a highly secure framework for carrying out visual surveillance on random looking video streams at remote servers. We then propose a simple and an efficient cloud-computing based solution using the paradigm of secret sharing to privately cluster an arbitrary partitioned data among N users. The solutions we propose are accurate, efficient and scalable and has potential to extend over to even more diverse applications. In our first work, blind authentication, we propose private biometric authentication protocol which is extreamly secure under a variety of attacks and can be used with a wide variety of biometric traits. The protocol is blind in the sense that it reveals only the identity, and no additional information about the user or the biometric to the authenticating server or vice-versa. The primary advantage of the proposed approach is the ability to achieve classification of a strongly encrypted feature vector using generic classifiers such as Neural Networks and SVMs. Our proposed solution addresses the concerns of user’s privacy, template protection, and trust issues. And captures the advantages of biometric authentication as well as the security of public key cryptography. We then present an efficient, practical and highly secure framework for implementing visual surveillance on untrusted remote computers. To achieve this we demonstrate that the properties of visual data can be exploited to break the bottleneck of computational and communication overheads. The issues in practical implementation of certain algorithms including change detection, optical flow, and face detection are addressed. Our method enables distributed secure processing and storage, while retaining the ability to reconstruct the original data in case of a legal requirement. Such an architecture provides us both security as well as computation and communication effic

Abstract

Repeats are ubiquitous in DNA sequences. When these arise in the coding regions, the proteins encoded by these genes exhibit various types of periodic patterns of amino acids, ranging from runs of single amino acids, e.g., polyglutamine tracts in the protein huntingtin, to short tandem peptide repeats, e.g., tandem octapeptide repeats in prion proteins and tandemly repeated domains, e.g., ankyrin domain in cytoskeletal protein titin, to periodic occurrences of single amino acids, e.g., glycines repeated at every third position in collagen proteins. Since the order of amino acids in a polypeptide sequence plays an important role in the fold and function of the protein, it would be interesting to understand the structural and functional role of the repeated sequence in proteins. Hence for the investigation and analysis of repeats in proteins, we have proposed a computationally efficient approach for identifying the periodic repeats and implemented it in a web-server, PEPPER [http://ccnsb.iiit.ac.in/nita/PEPPER/]. The first step in understanding the functional and evolutionary analysis of the protein repeats would be to identify them among homologous sets and organize them in a database. With this objective we also developed a database of repeats, DRiPs [http://ccnsb.iiit.ac.in/nita/PEPPER/PTRDB/], by scanning the complete protein sequence database, Swissprot, using PEPPER. A large number of novel periodic repeats, not reported in SwissProt, have been identified using PEPPER and can be accessed through the DRiPs database. A detailed analysis of the distribution of the three types of periodic repeats in terms of its dependence on the protein length, the repeat length, and the copy number of the repeat and their distribution across kingdoms has been carried out. From this analysis we observe that there is a high incidence of repeats in eukaryotic proteins compared to prokaryotic proteins and this cannot be attributed only to longer lengths of protein sequences in eukaryotes. This led us to identify the functions of eukaryotic proteins containing proteins and we observed that these proteins are involved in complex cellular functions unique to eukaryotes. To understand the occurrence of repeats in evolution, an analysis of the copy number of the repeat pattern across homologs was carried out to see if the repeats are conserved over evolution or is there a random gain or loss of repeat copies in homologous proteins. In most cases conservation/gain in the copy no. of the repeat was observed giving a clear indication of the possible function role of repeats. We also investigated the disease association of the repeat patterns using OMIM databases and Trbase databases, wherein the annotations were manually checked to see if the disease association reported is due to the repeat expansions or due to the protein misfold/aggregation.

Abstract

With the advent of the Internet/WWW and the proliferation of textual news, richmedia, social networking, et cetera. there has been an unprecedented surge in the content on the web. With huge amount of information available on the world wide web, there is a pressing need to have ‘Information Access’ systems that would help users with an information need in providing the relevant information in a concise, pertinent format. There are various modes of Information Access including Information Retrieval, Text Mining, Machine Translation, Text Categorization, Text Summarization, et cetera. In this thesis, we study certain aspects of “Text Summarization” as a technology for Information Access’. Majority of the recent work in the area of Text Summarization addresses the subproblems like Ranked Sentence Ordering, Generic Single Document Summarization, Generic Multi-Document Summarization, Query-Focused Multi-Document Summarization, Query-Focused Update Summarization. In this thesis we focus on two tasks, specifically “Query-Focused Multi-Document Summarization (QFMDS)” and “Query-Focused Update Summarization (QFUS)”. The QFMDS task has generated a lot of community interest for obvious reasons of being closer to a real world application of open domain question answering. Given a set of N relevant documents on a general topic and a specific query (or information need) within the topic, the task is to generate a relevant and fluent 250 word summary. The focus of this thesis lies around four issues dealing with query-focused multi-document summarization. They are: 1. Impact of query-bias on summarization 2. Simple and strong baselines for update summarization 3. Language modeling extension to update summarization 4. An automated intrinsic content evaluation measure In this thesis we identify two dependent yet different terms ‘query-bias’ and ‘query-focus’ with respect to the QFMDS task and show that most of the automated summarization systems are trying to be query-biased rather than being queryfocused. In the context of this problem, we show evidence from multiple sources to display the inherent bias introduced by the automated systems. First, we theoretically explain how a na¨ıve classifier based summarizer can be enhanced greatly by biasing the algorithm to use just the query-biased sentences. Second, on an ‘information nugget’ based evaluation data, we show that most of the participating systems were query-biased. Third, we further build formal generative models, namely binomial and multinomial models, to model the likelihood of a system being query-biased. Such a modeling revealed a high positive correlation between ‘a system being query-biased’ and ‘automated evaluation score’. Our results also underscore the difference between human and automated summaries. We show that when asked to produce query-focused summaries humans do not rely to the same extent on the repetition of query-terms. The QFUS task is a natural extension to the QFMDS task. In update summarization task a series of news stories (news stream) on a particular topic are tracked over a period of time and are available for summarization. Two or more sets of documents are available: an initial set, followed by multiple updated sets of news stories. The problem is to generate query-focused multi-document summaries for the initial set and then produce update summaries for the updated sets assuming the user has already read the preceding sets of documents. This is a relatively new task and our work in this context is two fold. First, we defined a sentence position based baseline summarizer which is genre dependent. In the context of the new task, we argue that current baseline in the update summarization task is a poor performer in content evaluations and hence cannot help in tracking the progress of the field. A stronger baseline such as the one based on “sentence position” would be robust and be able to help track the progress. Second, we describe a generic extension to language modeling based approaches to tailor them towards the update summarization task. We showed that a simple Context Adjustment (CA) based on ‘stream dynamics’ could help in generation of better summaries when compared to the base language modeling approach. In Text Summarization, like in any other Information Access methodologies, evaluation is a crucial component of the system development process. In language technologies research, Automated Evaluation is often viewed as supplementary to the task itself since knowing how to evaluate would lead to knowing how to perform the task. In the case of summarization, knowing how best to evaluate summaries would help in knowing how best to summarize. Usually, manual evaluations are used to evaluate summaries and compare performance of different systems against each other. However, these manual evaluations are time consuming and difficult to repeat, hence infeasible. Keep

Abstract

Proteins are the most complex organic compounds synthesized in nature and are an important class of biomolecules that serve as essential building blocks of the body. Proteins are organized into structural/functional units like motifs, domains and folds. So far, efforts to understand protein evolution and its functions have focused on domains that can fold into a stable threedimensional structure independent of the rest of the protein chain and perform a unique function conserved over the evolution. Genetic recombinant techniques allow reorganization of domains at the amino acid sequence level (such as circular permutations). This along with swapping and insertion of domains are responsible for new protein functions and evolution. Protein domains are very useful in analyzing the mechanisms of protein folding and their stability and structural transformations in various conditions. This can be done reliably if a multi-domain target is considered only in terms of its constituent domains. The underlying goal is to reduce a complex protein structure to a set of simpler yet structurally meaningful units, each of which can be analyzed independently. Being the basic units of protein folding, function and evolution, the identification and analysis of domains forms the first step in understanding the protein functional and structural aspects. Although the boundaries of a domain can be determined by visual inspection, there is an urgent need for the development of accurate methods for automatic domain identification as the number of solved protein structures is increasing rapidly. This problem of dividing a protein structure into domains is not yet solved efficiently due to the lack of an unambiguous definition of a domain. Existence of noncontiguous domains adds to the difficulty in developing an automated solution for domain partitioning. Though several methods have been proposed to predict domains they all have notable limitations. Even current reliable methods such as DomainParser, based on graph partitioning approach, do not distinguish between single and multi-domain proteins. Another recent graph-spectral approach by Sistla et al is unable to identify noncontiguous multi-domains and also does not distinguish between single and multi-domain proteins. With about 25% of multi-domain proteins being noncontiguous, there exists a need for reliable domain identification methods. Here we present a simple method based on graph spectral properties for automatic domain prediction from its 3-dimentional structure. We show that graph properties such as density and interaction strength apart from spectral analysis helps in domain identification. Since most single domains are typically less than 300 amino acids long, our first screening for single domain proteins is done on the basis of length. Next we show that the density shows a gradual decrease with the increase in the length of the protein, thus providing a useful parameter for domain identification. It is a common observation that the interactions between the amino acids are higher within a domain (intra-domain interactions) than across the domains (inter-domain interactions). We exploit this feature for identifying the domain boundaries and also for distinguishing between single and multi-domain proteins. In general, domains are identified either by: (i) Top-down approach, i.e., starting with the whole structure partition it iteratively into smaller units. (ii) Bottom-up approach, i.e., defining very small structural units and assembling them into domains, or (iii) a combination of both. In the proposed approach for domain identification, both, top-down and bottom-up approaches are implemented. The algorithm proceeds by first decomposing the protein contact network into compact structural modules and then assembling them into domains. These interactions and the topological details of protein structures can be effectively captured by a protein contact network, constructed by considering each amino acid as a node, and with an edge drawn between two nodes if the Cα atoms of the amino acids are within 7Å. Newman’s community detection approach proposed for social networks is employed here to identify domains in protein structures as we observe similarities between the two problems. It is particularly suitable for domain identification problem as one may not have a priori knowledge of the number of structural domains in a protein. The basic principle of this approach is to divide the vertices into two groups so as to minimize the number of edges running between the groups, which is in agreement with the basic feature of domains that inter-domain connections are more in number than connections across domains. We have implemented this approach on protein contact networks and analyzed the eigenvectors of the largest eigenvalue of the modularity matrix (which is a modified form of the Adjacency matrix). First, a given pr