Abstract

Indian software industry has set up huge growth targets for future. These targets would be heavily affected by the software engineering (SE) education scenario in the country. The purpose of this paper is to provide a holistic understanding of SE education issues and challenges specific to Indian context, from both industry and academic perspective. This study is based on our (a) interaction with industry through SE education related projects, surveys and discussions. (B) Observations as an integral part of Indian SE educators' community. There is an urgent need for addressing these deep rooted issues, as the lack of proper SE education may be the single largest factor that may negatively affect the industry. Understanding these issues will help to identify the action items that initiate software engineering educational reforms in the country. We also discuss the essential and minimal set of SE knowledge, skills and dispositions that the industry expects from engineers willing to join the industry. Though Indian software industry is growing at phenomenal rates, there not many studies on the issues and effects associated with SE education in the Indian context. Though the discussion is limited to India, but we believe that it represents the existing conditions in many developing countries where IT and ITES (IT Enabled Services) industry is becoming important.

Abstract

Software Engineering (SE) and Information Technology (IT) jobs are the most sought after career options for Indian youth in the recent times. Indian Software industry is expected to grow at a very healthy rate and each major software company has ambitious plans and growth targets for future. However, lack of proper Software Engineering (SE) education may have severe consequences and may negatively affect these growth targets. In this paper we discuss challenges and issues related to software engineering education and training in Indian academia and industry. These are based on our interaction with industry and through our experience as Software engineering educators. These challenges arise from deep rooted issues in Software Engineering educational goals, pedagogy and instruction as well as the infrastructure. We will discuss their long term effects on various aspects of software development. We put forth our suggestions that may handle these challenges to an extent. We also discuss the essential and minimal set of software engineering knowledge, skills and dispositions that the industry requires from young engineers willing to join the industry. This paper provides course designing guidelines for the academia and learning centers of the industry by focusing on important SE education issues, their causes and possible solutions. This, in turn would help to make SE Education more effective and inline with requirements of the Indian Software industry.

Abstract

It is observed that information technology (IT) is being used to automate the traditional government processes and to achieve easy access of the information. They are developed to increase the efficiency of the government departments and represent their needs. As a result, they achieved a huge inventory of IT applications providing disparate processes implemented using different technologies and platforms. It thus became very important to use IT as an" integrator" allowing disparate processes running in different departments to interconnect. This helps the citizen to access different government services through a common single-point window and helps the government to transform from government-centric organization to citizen-centric organization

Abstract

In this paper we present a statistical transliteration technique that is language independent. This technique uses Hidden Markov Model (HMM) alignment and Conditional Random Fields (CRF), a discriminative model. HMM alignment maximizes the probability of the observed (source, target) word pairs using the expectation maximization algorithm and then the character level alignments (n-gram) are set to maximum posterior predictions of the model. CRF has efficient training and decoding processes which is conditioned on both source and target languages and produces globally optimal solutions. We apply this technique for Hindi-English transliteration task. The results show that our technique perfoms better than the existing transliteration system which uses HMM alignment and conditional probabilities derived from counting the alignments.

Abstract

Graphs can be used for effective representation of images for recognition and retrieval purposes. The problem is often to find a proper structure that can efficiently describe an image and can be matched in reasonably low computational expense. The standard solutions to the graph matching problem are computationally expensive since the search space involves all permutations of the nodesets. We compare two graphical representations called the Nearest-Neighbor Graphs and the Collocation Trees, for the goodness of fit and the computational expense involved in matching. Various schemes to index the graphical structures have also been discussed.

Abstract

This paper presents the experiments of Language Technologies Research Centre (LTRC) as part of their participation in CLEF2 2007 Indian language to English ad-hoc cross language document retrieval task. In this paper we discuss our Hindi and Telugu to English CLIR system and the experiments using CLEF 2007 dataset. We used a variant of TFIDF algorithm in combination with a bilingual lexicon for query translation. We also explored the role of a document summary in fielded queries and two different boolean formulations of query translations. We find that a hybrid boolean formulation using a combination of boolean AND and boolean OR operators improves ranking of documents. We also find that simple disjunctive combination of translated query keywords results in maximum recall.

Abstract

The Oxford/IIIT team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or audio information. For the high-level feature extraction task, we used two different approaches, both based on a combination of visual features. One used a SVM classifier using a linear combination of kernels, the other used a random forest classifier. For both methods, we trained all high-level features using publicly available annotations [3]. The advantage of the random forest classifier is the speed of training and testing. In addition, for the people feature, we took a more targeted approach. We used a real-time face detector and an upper body detector, in both cases running on every frame. Our best performing submission, C OXVGG 1 1, which used a rank fusion of our random forest and SVM approach, achieved an mAP of 0.101 and was above the median for all but one feature. In the interactive search task, our team came third overall with an mAP of 0.158. The system used was identical to last year with the only change being a source of accurate upper body detections.

Abstract

Web search personalization has been well studied in the recent few years. Relevance feedback has been used in various ways to improve relevance of search results. In this paper, we propose a novel usage of relevance feedback to effectively model the process of query formulation and better characterize how a user relates his query to the document that he intends to retrieve using a noisy channel model. We model a user profile as the probabilities of translation of query to document in this noisy channel using the relevance feedback obtained from the user. The user profile thus learnt is applied in a re-ranking phase to rescore the search results retrieved using an underlying search engine. We evaluate our approach by conducting experiments using relevance feedback data collected from users using a popular search engine. The results have shown improvement over baseline, proving that our approach can be applied to personalization of web search. The experiments have also resulted in some valuable observations that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection

Abstract

Many events are captured using multiple cameras today. Frames of each video stream have to be synchronized and aligned to a common time axis before processing them. Synchronization of the video streams necessarily needs a hardware based solution that is applied while capturing. The alignment problem between the frames of multiple videos can be posed as a search using traditional measures for image similarity. Multiview relations and constraints developed in Computer Vision recently can provide more elegant solutions to this problem. In this paper, we provide two solutions for the video frame alignment problem using two view and three view constraints. We present solutions to this problem for the case when the videos are taken using affine cameras and for general projective cameras. Excellent experimental results are achieved by our algorithms.

Abstract

This paper discourses our CLIR experiments performed for the FIRE1 workshop. We had submitted our runs for Adhoc monolingual document retrieval in Hindi and English, and Ad-hoc cross-lingual document retrieval from Hindi to English, and English to Hindi. In this paper, we describe our English to Hindi and Hindi to English CLIR systems and the experiments conducted on them using the FIRE-2008 dataset. We had used a dictionary-based approach of query translation and transliteration of named entities in the queries using a mapping-based, Compressed Word Format (CWF) algorithm [1]. Disjunctive Query formulation with different scoring weights based on the part of the topic from which they originated gave us overall better performance.