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With the development of the computer technology a larger of data is generated and stored and many companies become ‘data rich and information poor’. This paper describes how BT has used data mining techniques to convert volume data into high-value information which can be used to aid decision making in a number of key business processes. The benefit of actively using data, as opposed to passively storing it, is demonstrated via a number of case studies which cover areas as diverse as fault diagnosis, fraud detection, market segmentation, credit vetting and litigation assessment.

In recent years, e-learning system has become more and more popular and many effective assessment systems have been proposed to offer students for convenience of their self-assessment. However, conventional test systems simply provide students a score, and do not provide adaptive learning guidance for students. Thus, how to automatically diagnose student's learning status and provide learning help becomes an interesting issue. This study proposes an assessment model based on Bayesian Networks, which assesses learning status by knowledge map after absorbing and analyzing test results. In order to form adaptive and tailored feedback, rule inference and exact inference are applied to combine Knowledge map with teacher's experience rules. Experimental results have demonstrated that the novel model benefits students and deserves further investigation.

In this paper we propose an Intra mode decision method for the MPEG-2 to H.264/AVC transcoders to reduce the huge computational complexity. The DCT coefficients from the MPEG-2 decoder are used for the intra mode decision of H.264/AVC encoder. Based on the spatial activity analysis with the coefficients, early mode decision method between Intra16x16 and Intra4x4 modes and the Intra prediction direction are proposed. The empirical results show that the proposed method reduces about 70% computational complexity reduction compared with conventional method at the slight reduction in PSNR.

Plagiarism in students programming assignment submissions causes considerable difficulties for course designers. Efficient detection of plagiarism in programming assignments of students is important to the educational procedure. This paper proposes a metric, based on information distance, to measure similarity between two programs. Furthermore, clustering analysis, based on shared near neighbors, is applied in order to provide more beneficial and detailed information about the program plagiarism. Experimental results demonstrate that our software has clear advantages over other plagiarism detection systems and it is quite beneficial to teachers to get rid of time-consuming and toilsome tasks. Key words: Program plagiarism, Detection, Information distance, Clustering

A web-based operational skills examination and evaluation system is designed and implemented for computer courses. It consists of four systems, including preparation, examination, monitor and auto-grading subsystem. Various techniques involving DCOM, mark-method and fuzzy-match are adopted in this system, and a universal approach is generalized to enable auto-grading system suitable for different operated results. This system has been successfully applied in operational skills’ evaluation and training, such as programming, editing documents, using Microsoft Windows.