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Abstract

With the rapid growth of the Internet, most of the textualdata in the form of newspapers, magazines and journals tend to be avail-able on-line. Summarizing these texts can aid the users access the in-formation content at a faster pace. However, doing this task manuallyis expensive and time-consuming. Automatic text summarization is asolution for dealing with this problem. For a given text, a text summa-rization algorithm selects a few salient sentences based on certain fea-tures. In the literature, weight-based, foci-based, and machine learningapproaches have been proposed. In this paper, we propose a popularity-based approach for text summarization. A popularity of the sentence isdetermined based on the number of other sentences similar to it. Usingthe notion of popularity, it is possible to extract potential sentences forsummarization that could not be extracted by the existing approaches.The experimental results show that by applying both popularity andweight-based criteria it is possible to extract effective summaries.

Abstract

Ranking the search results is an important research problem in WWW. HITS, PageRank and variations of these algorithms are widely used approaches for ranking. In this paper we proposed a new ranking algorithm to rank the search results by introducing the concept of “leader-page”. The notion of “leader-page” is defined by extending the concept of “leader” from leadership theory. In leadership theory, a leader is defined as a person, who has more contacts with the other members of the group, both initiates and receives communication, and whose characteristics are the most similar to the group’s own characteristics. Similarly, in WWW, the proposed approach identifies leader-pages and assigns “leadership score” to them based on several kinds of cyclic and similarity relationships it establishes with other web pages. Given a set of key words (search query), the proposed approach ranks the related pages based on the corresponding “leadership score”. The experiment results show that the proposed approach gives high leadership score to resourceful pages as compared to the results of the HITS algorithm and the Google search engine

Abstract

The current approaches to the software engineering education fall short to fulfill the industry demand for quality software engineering. A constant need to create and imbibe more effective learning environments is growing in order to manage this demand. This paper discusses the learning disabilities possessed by both the conventional and the non-conventional approaches for teaching software engineering. We propose that case studies can be used as effective teaching mediums and a case study centric learning environment can address these learning disabilities. A case study approach can help the students to gain and retain realistic exposure to concepts of software engineering as they are applied in the real world, and the students of today can be groomed as excellent professionals who have experienced the intricacies and complexities of the real world as well as tried their hands to manage these complexities.

Abstract

Conceptual blending is an important area of research for creativity modeling. In this paper we present a creativity model that takes an existing blend and generates new blends using the nearest neighborhood replacements from a lexical ontology. For example “Artificial Intelligence” is a compounded concept comprising of “Artificiality” and “Intelligence” as two sub-concepts. After concept generation, the system comes up with “Artificial Creativity” as one of the generated concepts. This project is part of a larger ongoing project called CAPRICON whose goal is to provide with stimuli for new futuristic concepts. This tool is a creativity enhancement tool that helps users enhance their creative thinking by providing stimuli for new concepts.

Abstract

Abstract The task in Document Understanding Conferences (DUC1) 2005 is to generate fixed length, user oriented, multi document summary. Our approach to address this task is primarily motivated by the observation that metrics based on key concepts overlap give better results when compared to metrics based on n-gram and sentence overlap. In this paper, we present a sentence extraction based summarization system which scores the sentences using Relevance Based Language Modeling, Latent Semantic Indexing and number of special words. From these scored sentences, the system generates a summary of required granularity. Our summarization system was ranked 3rd, 4th, 8th and 17th in ROUGE-SU4, ROUGE-2, responsiveness and linguistic quality evaluations respectively. In post DUC analysis we found that LSI has negative effect on the systems performance, and the performance gained by 5.4% when it is implemented using language modeling and number of special words.

Abstract

Performance of hierarchical classifiers depends on two aspects – the performance of the individual classifiers, and the design of the architecture. In this paper, we present a scheme for designing hybrid hierarchical classifiers under user specified constraints on time and space.

Abstract

With the advancement of multimedia, digital video creation has become very common. There is enormous information present in the video, necessitating search techniques for specific content. In this paper, we present an approach that enables search based on the textual information present in the video. Regions of textual information are identified within the frames of the video. Video is then annotated with the textual content present in the images. Traditionally, OCRs are used to extract the text within the video. The choice of OCRs brings in many constraints on the language and the font that they can take care of. We hence propose an approach that enables matching at the image-level and therby avoiding an OCR. Videos containing the query string are retrieved from a video database and sorted based on the relevance. Results are shown from video collections in English,Hindi and Telugu.

Abstract

In this paper we describe a prototype system that recognizes text in images, captured by camera phones and provide access to the content in a different language. Two Indian languages – Hindi and Tamil are used to demonstrate such a system. The prototype system is built using off-the-shelf components, and in house developed algorithms. The acquired image is first transferred to a server, which corrects the perspective distortions, detects recognizes and then translates the text (word). This translated text is sent back to the camera-phone in a suitable form. We have also described here, the Hindi and Tamil OCRs which we use for the character recognition. We also propose methods to make the recognizer efficient in storage and computation. The translated text, along with any additional information, is transmitted back to the user.

Abstract

A discriminant-based framework for automatic recognition of online handwriting data is presented in this paper. We identify the substrokes that are more useful in discriminating between two online strokes. A similarity/dissimilarity score is computed based on the discriminatory potential of various parts of the stroke for the classification task. The discriminatory potential is then converted to the relative importance of the substroke. Experimental verification on online data such as numerals, characters supports our claims. We achieve an average reduction of
 in the classification error rate on many test sets of similar character pair