Research Papers - Dept of Software Engineering

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End date: 2009

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    Integrating industrial technologies, tools and practices to the IT curriculum: an innovative course with .NET and java platforms
    (acm.org, 2005-10-20) Athauda, R; Kodagoda, N; Wickramaratne, J; Sumathipala, P; Rupasinghe, L; Edirisighe, A; Gamage, A; De Silva, D
    Exposure to state-of-art industry technologies, tools and practices by students provide CS/IT graduates highly desirable skills and marketability. A key expectation of the industry from their new cadre is a speedy integration into the business environment resulting in productive work. This usually requires having a sound technological background, a maturity to assess the environment and adapt quickly, and highly-developed soft skills to be productive in a team environment. Incorporating such experience and skills into a CS/IT curriculum is challenging and is still in its infancy stages. We undertook such as an endeavor in integrating .NET into the IT curriculum. Microsoft's .NET platform is becoming increasingly popular in the industry. Incorporating .NET into the undergraduate IT curriculum provides a plethora of skills and increases the employability of our graduates. We integrated .NET without a major revision to the existing curriculum by introducing an optional course in the final year (senior-level) of the IT undergraduate program. In addition to the .NET platform, the course covered the Java platform, which is similar in architecture to .NET. The course emulated an industry-based environment with real-world based assignments, focused on deliverables, used state-of-art IDEs and documentation, and pair programming to create a highly productive environment. The “soft skills” were integrated into the course with a project that implemented a virtual marketplace. Students in groups played different entities in the virtual marketplace and communicated with each other via Web Services. The project provided a virtual business environment and exposure to teamwork, collaboration, competition, negotiating, and creativity skills. Our first offering of the course in semester 1, 2005, attracted 128 students. The course created a highly productive environment throughout the semester. Students completed 7 assignments and the project within the 14-week semester. The initial results are encouraging and provide many insights to CS/IT departments planning to incorporate such courses.
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    Eigenface based automatic facial feature tagging
    (IEEE, 2008-12-12) Wijeratne, S; Jayawardena, S; Jayasooriya, S; Lokupathirage, D; Patternot, M; Kodagoda, N
    There are several approaches to search databases of faces. However such methods still require a significant use of humans to interpret an eyewitness account and so forth. In many cases these searches are done using visual building tools as creating a graphical face model. A system that can easily interface with general users should directly search a person by description given verbally or textually. This would reduce costs in the search process. Facial feature characteristics identification would act as a stepping stone in cataloguing large face databases automatically thus providing the possibility of a description based face search by text. This paper presents the possibility of utilizing eigenface approach to recognize different characteristics of a facial feature and assigning descriptive words such as "Large", "Small" to each feature. After training the system, it would automatically attempt to match a pattern in the training set that best describes the input image and output a tag associated with it. This effectively allows an image of a person's face to be tagged by his or her feature characteristics. While utilizing the standard set steps as defined in the eigenface algorithm, slight modifications are done in the algorithm that matches input images with ones in the training set. The training set defined has a very huge impact for the final outcome, and due to the subjective nature of the training, future research would be done on this regard. The investigation showed that the method works fine with well defined features such as eyes but fails for features such as foreheads due to the lack of significant differences or characteristics between such features. Hence it is seen that while eigenface can be used for the categorization of well defined features, it is unable by itself to create a system that can cover all features of a face.
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    Voizlock-human voice authentication system using hidden markov model
    (IEEE, 2008-12-12) Maduranga, R. G; Jayamaha, M; Senadheera, M. R. R; Gamage, T. N. C; Weerasekara, K. D. P. B; Dissanayaka, G. A; Kodagoda, N
    Speaker authentication is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. Many principles are used in the area of voice recognition. This paper provides a method of storing the voiceprints of individuals uniquely, based on the Hidden Markov Model. HMM has been used in the speech recognition area for a long period of time, but VoizLock project explores a way of using HMM for voice authentication which is different from speech recognition. This voiceprint will then be used for voice authentication, using text-independent speaker recognition methods in which the system does not rely on a specific text being spoken, but solely on the voice of the speaker. This paper also provides details about certain misconceptions with regard to voice authentication that exist in the society. This paper explains more about the user training phase detailing how the voice print of an individual is stored in the system by extracting certain values of the waveform using HMM. Apart from the training phase this analyses the results obtained from the testing done covering different scenarios pertaining to voice authentication.
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    Internal structure and semantic web link structure based ontology ranking
    (IEEE, 2008-12-12) Rajapaksha, S. K; Kodagoda, N
    The semantic Web is an extension of the World Wide Web with new technologies and standards that enable interpretation and processing of data and useful information for extraction by a computer. The World Wide Web Consortium (W3C) recommends XML, XML schema, RDF, RDF schema and Web Ontology Language (OWL) as standards and tools for the implementation of the semantic Web. Ontologies work as the main component in knowledge representation for the semantic Web. It is a data model that represents a set of concepts and the relationships between those concepts within a domain. Building an ontology starting from scratch is not an easy task since it makes heavy demands on time in addition to expert knowledge related to the domain. However, we can use the existing ontologies to develop semantic Web applications. But, there are a large number of ontologies available and the ontology search engine will generate a bulk of results with different ontologies for search queries. Therefore, ranking of ontologies is needed to find the most appropriate and relevant ontologies. We consider the ranking techniques and algorithms attached to the semantic Web: (i) Swoogle Ranking (ii) Ontokhoj Ranking (iii) OntoQA Ranking (iv) AKTiveRank (v) OntoSearch Ranking (vi) content-based ontology ranking (vii) SemSearch Ranking (viii) ReConRank. Our effort considers most popularly used ranking techniques and algorithms attached to the semantic Web. We analyze the above ontology ranking techniques with algorithms and then mainly categorize into two groups. One group is based on the semantic Web link structure and the other one is based on internal structure of the ontology. We identify that some features are not addressed in ranking of ontologies selected by the above ranking techniques and algorithms. Therefore, we propose a ranking method that considers both internal structure and semantic Web link structure of ontologies to improve the ranking of ontologies. We finally evaluate the proposed ranking method. According to the results with evaluation, we allocate more weighting for internal structure and low weighting for semantic Web link structure to get the best ranking results.