Abstract

Indian languages have a rich literature that is not available in digitized form. Attempts have been made to preserve this repository of art and information by maintaining a digital library of scanned books. However, this does not fulfill the purpose as indexing and searching the documents is difficult in images. An OCR system can be used to convert the scanned documents to editable form. However, the OCR systems are error prone. These errors are largely unavoidable and occur due to issues like poor-quality images, complex font, unknown glyphs etc. A post-processing system can help in improving the accuracy by using the information about the patterns and constraints in the word and sentence formation to identify the errors and correct them. OCR is considered to be a problem attempted with marked success in Latin scripts, especially English. This is not the case with Indic scripts as the error rates of various OCR systems available are comparatively high. The OCR pipeline includes three main stages, namely segmentation, text recognition and post-processing. We observe that Indic scripts have complex scripts and glyph segmentation itself is a challenge. The existence of visually similar glyphs also makes the recognition process difficult. The challenges faced in the post-processing stage are largely due to the properties of Indian languages. The inflectional properties of some languages like Telugu and Malayalam and agglutination of words creates issues due to the enormous and growing vocabulary in these languages. Unlike alphabet system in English, Indic scripts follow alphasyllabary writing system. Hence the choice of unicodes as the basic unit of a word is questionable. Aksharas which are a more meaningful unit is considered as a better alternative to unicodes. In this thesis, we analyze the challenges in building an efficient post-processor for Indic language OCR s and propose two novel error detection techniques. The post-processing module deals with the detection of errors in the recognized text and correction of those detected errors. To understand the issues in post-processing in Indian languages, we first perform a statistical analysis of the textual data. The unavailability of huge corpus prompted us to crawl various newspaper sites and Wikipedia dump to obtain the required text data. We compare the unique word distribution and word cover of popular Indian languages with English. We observe that languages like Telugu, Tamil, Kannada and Malayalam tend to have huge number of unique words compared to English. We also observe how many words get converted to other valid words in the language, using the Hamming distance between the words as a measure. We empirically analyze the effectiveness of statistical language models for error detection and correction. First we use an ngram model for detection of errors in the OCR output. We use akshara splitwords to create a bigram and trigram language model which gives the probability of a word. A word is declared as an error word if it has lower probability than a pre-computed threshold value. For error correction, we replace the lowest probability ngram with a higher probability one from the ngram list. We observe that akshara level ngrams perform better than unicode level ngram models in both error detection and correction. We also discuss why the dictionary based method, a popular method used in English, is not a reliable solution for error detection and correction in case of Indic OCR s. We use a simple binary dictionary method for error detection, wherein if the word is present in the dictionary, it is tagged as a correct word and error otherwise. The major bottleneck in using a lexicon is the enormous words in the languages like Telugu and Malayalam. In error correction, we use Levenshtein and Gestalt scores to select the candidate words from the dictionary for replacement of error word. Inflection of words causes issues in selecting the correct words as the candidate list consists of many words which are close to the error word. We propose two novel methods for detecting errors in the OCR output. Both the methods are language independent and does not require knowledge of language grammar. For detecting the errors in the OCR output, the first method proposed uses a recurrent neural network to learn the patterns of errors and correct words in the OCR output. The second method is using a Gaussian mixture model based clustering technique. Both methods use a language model of unicode as well as akshara split words in creating the features. We argue that aksharas are a better choice as the basic unit of a word than unicode. An akshara is formed by the combination of one or more unicode characters. We tested our method on four popular Indian languages and report an average error detection performance above 80% on a dataset of 5 K pages recognized using two state of the art OCR systems.

Abstract

This thesis investigates different approaches proposed in literature to identify the semantic relations in nominal compounds. Nominal compounds have received a great deal of attention in the last decade,owing to two main reasons - the increasing frequency with which nominal compounds occur in language, and the challenges they pose in most natural language processing applications like information extraction and retrieval, document summarization, machine translation and the like. Interpreting the semantics of nominal compounds in particular has been a popular research topic in recent times for reasons beyond just the impactful presence of compounds, like the challenges in explicating the semantic relation between constituents of the compound which is not-so-obvious to a machine as it is to a native speaker of the language. Most of the approaches proposed prior to this work are predominantly statistical techniques or treat compound interpretation as a classification task using a standard set of semantic relations as the prediction labels. The drawbacks of purely statistical and computational approaches is that they often a require large training dataset or corpus with a good distribution across all types of semantic relations, and often have no underlying lexical knowledgebases to support any data sparsity issues that may arise. Some of these approaches even involve preprocessing of large and unstructured databases like dictionaries and corpora which is inefficient and time-consuming. I motivate the need for exploring a new school of thought, quite opposite to the approaches that use computations backed up by knowledgebases . I explore in this thesis, the creation of hybrid systems that mine information from lexical and semantic knowledge resources like ConceptNet and WordNet, and are sufficiently backed by computational measures as necessary, to handle compounds outside the scope of these resources. Such systems overcome the obvious drawbacks of purely statistical and supervised learning tasks. I discuss two popular generic-domain ontologies called WordNet and ConceptNet in terms of the representational schema adopted in these ontologies to capture nominal compounds, and from the perspective of using these ontologies for labeling compounds with semantic relations. Most frequent issues encountered with these ontologies were identified as the lack of sufficient semantic information for certain nominal compounds, and the incompleteness of these ontologies in terms of capturing all the different categories of nominal compounds using a generic representational schema. I propose a new representation schema that identifies uniqueness of compounds and build a well structured purpose-centric ontology called PurposeNet centered around this representational schema. The ontology-search approach proposed in this thesis uses simple lookup techniques on indexed and preprocessed PurposeNet to derive the semantic relation between head and modifier in a nominal compound. The same ontology-search was then tailored to suit the representation used in WordNet and ConceptNet too. Comparison of results of the ontology search on all the three ontologies indicates that the best performance was on PurposeNet. While the results are definitely motivating, the ontology-search approach is not robust enough to handle different types of nominal compound constructions in the generic domain. I show that combining this ontology-search approach with semantic relatedness measures based on WordNet makes our compound interpretation system robust due to collation of semantic information from multiple resources. This hybrid system achieves state-of-the-art results on our standard test set of compounds. Lastly, I also study the feasibility of using the same approach based on PurposeNet and English WordNet as is, to label nominal compounds in other languages which have meager knowledge resources. I choose to extend this approach for nominal compounds in Hindi, by translating compounds from Hindi to English using different lexical resources like the Hindi WordNet 1 and IITB Hindi-Universal Word dictionary 2 and then running them through our hybrid compound interpretation system. While the results are not very encouraging due to various issues like change in the structure of compound after translation or lack of coverage for borrowed words in the lexical resources used, they nevertheless indicate that this approach is re-usable for interpreting compounds from other languages, with the help of context-based translation systems that take into consideration the context and structure of the nominal compound in its source language.

Abstract

Short texts play a major role in our day-to-day communication in many forms such as emails, chat, tweets, news headlines, image captions and many more. Many techniques have been developed in the field of natural language processing to automatically process such text at scale and apply it in areas apart from other NLP applications like education, law, healthcare, social platforms and security. This thesis presents automatic identification of semantic similarity between two short texts. There is a large usage of short texts and messaging in our real life. We explore and study dialogues and propose techniques for the automatic identification of subtopic boundary in Hindi dialogue using semantic similarity as a measure. We observe that there is a need for a semantic textual similarity system for Indian languages. One of the direct applications of a semantic textual similarity system is in dialogue based intelligent tutoring systems, where the answer of the user is assessed by comparing its similarity with the answer given by experts. Identification of short text similarity is an important research problem with application in a multitude of areas. Machine translation evaluation is another example, where the textual similarity between the system output and the gold standard output is used to evaluate the accuracy of the system. The direct application of STS is also in answer sentence identification in question answering applications. In natural languages, similar ideas can be linguistically expressed in very different ways, thereby making the task of semantic similarity challenging. For European languages, the task of semantic textual similarity has been studied to a great extent and various techniques have been proposed for those languages, but for Indian languages work has been very limited. This research describes our work on semantic textual similarity by proposing an annotation scheme for grading semantic similarity between two sentences. We also propose and describe methods for automatic identification of semantic similarity between short texts for Hindi language. The proposed STS algorithms are applied to tasks such as machine translation evaluation and subtopic boundary identification.

Abstract

Penn Treebank has proved the importance of treebanks as a linguistic resource for NLP. The current research presents an effort to develop a pilot treebank for Hindi, which could be used for creating a large scale treebank for Hindi. Building a treebank requires a computational grammar framework, an annotation scheme based on a chosen grammar, guidelines for annotating various types of constructions in the concerned language, and other related resources such as verb frames, etc. Since Hindi has a relatively free word order, dependency grammar formalism is well suited for it. So we chose Computational Paninian Grammar framework [36]. Panini’s grammar is a dependency grammar [99, 162]. Hence, the scheme for annotating treebanks for Indian languages was developed based on this framework. As part of this study, a pilot treebank for Hindi (HyDT – Hyderabad Dependency Treebank for Hindi) [21] was developed which was released for ICON-2009 (International Conference on Natural Language Processing-2009) [86]. The scheme [21] and guidelines for treebank annotation for Hindi developed during this study were modified and are being used for a multi-layered and multi-representational treebank for Hindi and Urdu [39, 42, 188] which is a collaborative project between various Universities. As part of this study, I annotated about 2230 sentences of Hindi from CIIL (Central Institute of Indian Languages, Mysore) corpus. Some part of data annotation was done by another annotator whose annotation was later validated by me. Various issues encountered during the annotation were studied and resolved to strengthen the scheme. I discuss some of the Hindi constructions which were intensely analyzed such as Causatives, Relative clauses, Conjunct verbs, Perception verbs, Conditionals, etc. We also looked in detail at how to treat ellipsis in a dependency Treebank (HyDT). I then present the detail study of Causative verbs [20] and Conjunct verbs [19] in which I show the classification of Hindi causative verbs and the diagnostics used to identify the conjunct verbs, respectively. The study of these Hindi constructions helped us in updating the existing guidelines for further annotation. Along with the creation of Hindi Treebank (HyDT), I also created a supplementary resource of verb frames for 687 Hindi verbs. I present the work on verb frames [22] for Hindi verbs and show the methodology used in preparing these frames and the criteria followed for classifying Hindi verbs. Verb frames were developed following Paninian Grammatical framework. Verb frames provide us the arguments that a particular verb can take in a particular sense, i.e., they show mandatory dependency relations for a verb. The main goal of this work is to create a linguistic resource which will prove to be indispensable for various NLP applications. It is also helpful in preparing demand chart for Hindi parser [84, 25]. It is also helpful for the annotators in deciding various dependency relations for a given verb in the corpus. I have also worked on the mapping between Propbank annotation and dependency annotation, based on Paninian Grammatical Framework [21, 36]. I have also discussed the use of HyDT data (Hyderabad Dependency Treebank for Hindi) [21] in various experiments.

Abstract

With the increasing computational complexity, hardware accelerators are finding lot of usage in improvising holistic system performance. The scalable multi-core processor architecture together with hardware accelerators is able to significantly boost up the system performance in high-speed computing. Such hardware accelerators require a connectivity between the processor system and the accelerating hardware for communicating data and control signals. Depending on the system level requirement, a se- rial or a parallel interface can be defined between the processor system and hardware accelerator block. The light weight serial communication protocol described here can be used to establish high speed serial connectivity between the System-on-Chip (SoC) and the hardware accelerator platform. Using the pro- tocol described in this work can transfer data up to 400 Gbps which makes the protocol suitable for high bandwidth applications. With the new protocol, the author has observed 3X reduction in area and 1.4X gain in latency over the other serial communication protocols. Parallel communication requires greater infrastructure cost due the connectors and board space used for communication. Using serial commu- nication channel, it is possible to reduce the overall system cost while maintaining data integrity. High Speed serial IOs with embedded transceiver blocks are used for serial communication. Communication using high speed serial IOs requires an initialization procedure to ensure integrated data transfer across the serial links. A physical layer initialization protocol defines the link establishment steps required before the actual data transfer. The overhead associated with the serial link initialization depends on variety of factors including the type of encoding used for transferring data across the link, initialization steps which confirm that the link partners are ready to exchange data, channel bonding procedure to in- tegrate multiple serial links to achieve higher bandwidth etc. In this work, I have discussed about a novel approach for initializing serial communication channel using 64B/66B encoding technique and a light weight channel bonding methodology to minimize the overall initialization latency while minimizing the logic area and static and dynamic power consumption.

Abstract

Machine Translation among Indian languages is a challenging problem, owing to multiple factors like their morphological complexity and diversity, in addition to lack of sufficient parallel data for most language pairs. Recent advances in the past have employed rule-based and statistical techniques to approach the problem of Indian language MT. Neural Machine Translation is an emerging technique depicting impressive performance, better than traditional MT methods in multiple aspects. This thesis demonstrates the application of Neural Machine Translation (NMT) techniques for Indian languages, with an emphasis in two important directions:- 1. Usage of specific linguistic features belonging to Indian languages to improve translation quality. 2. Building a robust NMT model which delivers efficient performance across different domains with a limited parallel corpus. We create NMT systems for 110 Indian language pairs utilizing various morphological and syntactic features to improve translation quality. We observe that although NMT models have a strong efficacy to learn language constructs, the usage of specific features further help in improving the performance. We also propose a three-phase integrated approach which helps in improving robustness across domains as well as translation quality in the absence of large parallel corpora. The three-phase training shows a significant improvement in accuracy as well as coverage over a baseline NMT model. This is the first effort towards developing Neural Machine Translation for Indian languages to the best of our knowledge.

Abstract

The vast scale information available online is a rich source of collective human knowledge. The major transition from conventional information sources such as books and archives to online sources such as web pages, etc. has introduced the need to categorize, organize and mine the digital corpus since the advent of the internet age. Immense effort has been put into this direction since the last decade or so resulting in a lot of improvement in mining conventional documents. One of the major improvement in better understanding of textual documents has been because of Topic Models such as LDA (Latent Dirichlet Allocation) which are used to learn and highlight abstract predominant topics in the documents. However, more recently, due to advances in technology, new age social media platforms have equipped their users to communicate, network and share knowledge but changed the way information is being generated, propagated and consumed. It has not only affected the means of information sharing but also the characteristics of the documents in terms of their language, style, size and vocabulary. The document size and style pose several challenges to conventional data mining tools to be used to their full potential because they rely on document level word co-occurrences to learn important topics in the corpus. They need larger documents compared to a “typical” social media post. In this thesis, we focus on improving the performance of topic models for contemporary documents and text corpus generated in the form of social media posts. We discuss and propose an improvement to previous efforts in the direction and also introduce a new algorithm, sentence2cluster, which also helps in document categorization and organization. Our first proposition to improve the topic models is an extension of previous efforts, which mainly focuses on prepossessing of the documents in terms of document pooling or aggregation. A popular aggregation scheme is user-based document pooling, where the underlying assumption is that a person has an interest in a limited number of topics, therefore, it is useful to club all his documents into one big document. We extend this approach and propose to aggregate documents not only for a single user but also for users who share similar interests. We use many indicators such as the use of hashtags, communication amongst users, influences, etc. to identify a community of users who can pool their documents to learn a potentially better topic model. This community-based pooling instead of a single user based pooling not only increases the context and document size but also helps highlight niche topics which are popular only among a small group of people who are not as loud as other users. We run our experiments on Flickr datasets where we find communities of users who can pool their documents. We use network information and many user attributes to find such communities. We show that topics learned after community-based document aggregation are more coherent than other previously proposed document aggregation schemes. The second effort in the direction of improving Topic Models is the use of word embeddings. Topic modeling algorithms such as LDA do not use the information hidden in the order of the words in a sentence. In our new approach sentence2cluster, we use this information to learn meaningful word representations, followed by clustering them. The word clusters represent the broader corpus level topics which are assumed to be found in documents with different proportions. The idea to learn corpus level topics first and then infer their presence in every document is inspired from Bi-Term Topic model (BTM), which also focuses on improving topic models for short text but does not utilize information hidden in word ordering in a sentence. We evaluate the utility of the topics learned through our approach by building a navigated corpus browser on real world documents posted online on various social media platforms and forums. We then conduct a survey seeking user experience and to get a feedback on interpretability and coherence of topics and effective utility of our approach. The results indicate the potential in using word level semantics for topic modeling for short textual documents and the positive feedback that supports our hypothesis. Our work presents an elaborate discussion on various aspects of Topic Modeling and how they can be improved. We focus both on pre-processing steps using better aggregation schemes and on leveraging information hidden in sentence structure and word level semantics. In this manner, we hope that it drives fruitful research in various tasks of information categorization and organization in the future by drawing attention towards better Topic Models for contemporary texts.

Abstract

Fast side chain conformational dynamics play a vital role in the biological functions of proteins. High-resolution nuclear magnetic resonance (NMR) studies continue to evolve in important ways to elucidate the structure, dynamics and thermodynamics of proteins in natively folded, unfolded, invisible excited states, and in ligand-bound complexes. NMR spectroscopy uses methyl spin probes of methyl-containing residues to characterize the sitespecific side chain motions and to establish a molecular-level connection between the dynamics and thermodynamics of proteins. Since the side chain motions and thermodynamics are governed by the underlying conformational energy landscape, it is essential to understand the interrelationship between protein energy landscape, protein-mediated biological processes, side chain dynamics and their responses to external perturbations. The major aim of this thesis is to investigate the origin, diversity, functional roles of the fast side chain motions of proteins and their responses to external perturbations (ligand-binding and pressure) using molecular dynamics simulations. The universal features of fast side chain motions in proteins are characterized by examining the conformational energy surfaces of individual residues obtained using enhanced sampling molecular dynamics simulation. The free energy barriers separating side chain rotamer states follows a trimodal distribution. A hierarchical grading of rotamer states based on the conformational free energy barriers, conformational entropy (Scon f), and probability flux revealed three distinct classes of side chains in proteins. A unique nonlinear correlation is established between NMR order parameters (O 2 axis ) and the side chain rotamer populations (ρ). The apparent universality in O 2 axis versus ρ correlation, trimodal barrier distribution, and distinct characteristics of three classes of side chains observed among proteins with different secondary structures and molecular weights indicated a hidden regularity (or commonality) in the dynamical heterogeneity of fast side chain motions in proteins. The influences of ligandbinding, pressure and noncovalent interactions on the fast internal motions of proteins are examined. The derived correlations between O 2 axis, Scon f and strengths of noncovalent interactions reveals that side chain flexibility and conformational entropy decrease with increasing 9 strength of its noncovalent interaction with surrounding evironment in a sigmoidal-like fashion for all residue types. The results also showed that there exists a critical range of noncovalent interaction strength for each residue type determined primarily by the mean side chain conformational barriers, beyond which side chain flexibility and conformational entropy are insensitive to noncovalent interactions. Using fundamental statistical mechanical principles, an exact relationship between noncovalent interaction strengths, the relative stabilities of side chain conformational states and rotamer barriers of protein side chains is derived. Using calmodulin (CaM) and catabolite activator protein (CAP) complexes as model systems, the site-specific ligand-induced changes in thermodynamical properties and side chain dynamics of proteins are investigated. The distribution of rotamer barriers follows a trimodal distribution both in ligand-free and ligand-bound states of CaM and CAP. The distributions of changes in O 2 axis, for all complexes followed a Gaussian-like unimodal distribution and the width of the distribution varied among different complexes. Further, the side chains with O 2 axis ≈0.3 are observed to be least perturbed for all methyl-containing residues. A non-linear correlation between O 2 axis and the side chain rotamer population (ρ) and linear relationship between O 2 axis and conformational entropy (Scon f) are observed even in the presence of significant site-specific perturbations in protein complexes. The investigation on the effect of external pressure on the fast side chain dynamics reveals a heterogenous distribution of side chain dynamics and local compressibility of proteins. Structural characterization of ubiquitin at different pressures (0.001- 2.5 kbar) indicated that even at 2.5 kbar pressure it remains in its native folded state. The present thesis has illustrated the use of advanced enhanced sampling methods and molecular dynamics simulation to gain new molecular-level insights into fast conformational dynamics and to relate side chain dynamics and thermodynamics of proteins to the underlying conformational landscape.

Abstract

We tackle the problem of reconstructing moving vehicles in autonomous driving scenarios using only a monocular camera. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in emph{shape priors}, which are learnt over a small dataset comprising of annotated RGB images of vehicles. Each shape prior comprises of a deformable wireframe model whose vertices are semantically unique emph{keypoints} of that vehicle. The first contribution is an approach for reconstructing vehicles from just a single (RGB) image. To obtain a 3D wireframe representing the shape, we first localize the vertices of the wireframe (keypoints) in 2D using a Convolutional Neural Network (CNN). We then formulate a shape-aware optimization problem that uses the learnt shape priors to emph{lift} the detected 2D keypoints to 3D, thereby recovering the 3D pose and shape of a query object from an image. The shape-aware adjustment robustly recovers shape (3D locations of the detected keypoints) while simultaneously filling in occluded keypoints. To tackle estimation errors incurred due to erroneously detected keypoints, we use an Iteratively Re-weighted Least Squares (IRLS) scheme for robust optimization, and as a by-product characterize noise models for each predicted keypoint. We evaluate our approach on autonomous driving benchmarks, and present superior results to existing monocular, as well as stereo approaches. The second contribution is a real-time monocular object localization system that estimates the shape and pose of dynamic objects in real-time, using video frames captured from a moving monocular camera. Here again, by incorporating prior knowledge of the object category, we can obtain more detailed instance-level reconstructions. As opposed to earlier object model specifications, the proposed shape-prior model leads to the formulation of a Bundle Adjustment-like optimization problem for simultaneous shape and pose estimation. We demonstrate how these keypoints can be used to recover 3D object properties, while accounting for any 2D localization errors and self-occlusion. We show significant performance improvements compared to state-of-the-art monocular competitors for 2D keypoint detection, as well as 3D localization and reconstruction of dynamic objects

Abstract

Competent software engineers will produce quality software systems. Lack of such qualified software professionals has far reaching consequences and hence is a major worry for both academia and industry worldwide and especially in India. Educators are trying to create high impact learning environments that can address this specific issue. The general consensus among educators and learners is that there is a gap between theory and practice of Software Engineering (SE), addressable by incorporating more practice oriented instructional approaches in the learning environments, the conceptual embodiments of learning and teaching. The very idea has merit. However, instruction design for SE is not simple due to presence of several impacting factors, situated in the nature of software, Software Engineering, academia and software industry. Sustainability and cultural suitability are other factors to be considered. Since SE learning environments are high impact systems, their design and evaluation should be conscientious exercises, strongly supported by theory and practice, utilizing holistic yet systematic approaches. The primary goal of this work is to develop an effective and sustainable Software Engineering learning environment for Indian conditions. To address this quintessential problem, we followed a Design Based Research (DBR) approach that is suitable for educational research and exhibits many similarities with typical Software Engineering process. We examined the interdisciplinary domain of Software Engineering Education (SEEd) to identify factors impacting SE education in the Indian context. Accordingly, requirements related to learning, and of Differentiating nature were identified. Analysis suggested that the Differentiating requirements such as scalability, administrative usability, practiceorientation, motivation, feedback, pedagogical maturity, resource efficiency etc. exhibit significant interactions and determine the pedagogy design and validation in a learning environment. We could even classify the Differentiating requirements as Systemic and Climatic. We designed a learning model that can effectively and easily bring industrial knowledge and experience to the classroom, thus bridging the industry-academia gap. The proposed Case Oriented learning environment for Software Engineering Education (COSEEd) uses a case based instructional approach, with structured problem solving at its core. Essentially, challenges in well-designed case studies engage students in authentic SE activities for solving authentic, contextualized problems. Students achieve the learning goals through the process of understanding and solving a given SE case study. The assessment activities are in tight integration with the instruction, thus creating an environment conducive for learning. This layered system embeds multiple learning theories and design principles from Educational Technology to support the right set of qualities. To improve the specificity, accuracy and granularity of analysis, we developed a formal validation approach named as Requirement Satisfaction Index (RSI). RSI detects and measures the presence of requisite qualities, including learning at various cognitive levels. RSI is a novel way of thinking towards design and evaluation of learning environments in terms of needed capabilities or requirements of a learning environment. Results exhibit that COSEEd, the case study oriented learning environment is highly suitable for the Indian context. It imparts effective learning, is scalable, mature and flexible to vi accommodate the dynamic nature of SE. Several studies were conducted to examine the effectiveness of COSEEd. Results from these studies indicate the merit of the proposed learning environment on several cognitive measurements of learning, Climatic qualities and Systemic qualities. The model consistently scores high on learning objectives with higher-level cognitive goals. COSEEd’s advantages are visible in the Climatic requirements especially practice-orientation, use of authentic problems, interesting, and motivating. For Systemic requirements, the model was evaluated as scalable, flexible, mature, and administratively usable, though not resource efficient. We developed supporting technology and learning material in form of a set of case studies based on real projects from the Indian software industry. We identified mechanisms to impact learning by manipulating case studies and learning activities. Our approach differs from existing work in many ways. It is India specific, but is generalizable. We take a holistic approach towards the learning environment and consider both learning effectiveness and sustainability. Requirements of the environment are gathered after a thorough examination of Indian academic and industrial scenario as well as nature of the discipline. The design is rooted in several learning theories and design principles from