• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.49-55
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• 2007

This paper presents a method of score counting for computer Go that includes the consideration of stability, management of dead stones, and an algorithm for score counting. Thus, method for managing dead stones, filling all dames, and making additional moves is presented, along with a score-counting algorithm, where dames are defined as empty points that are not included in the area of a group, while additional moves are required for life when filling all the dames. In experiments using the final positions of 362 games, a mean error of 8.66, 5.96, and 4.15 was recorded for the score counting produced by the CGoban, HandTalk, and proposed methods, respectively. The proposed method was confirmed by experiments where it was success fully applied to the final positions.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.453-462
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• 2009

Studies on software plagiarism detection, prevention and judgement have become widespread due to the growing of interest and importance for the protection and authentication of software intellectual property. Many previous studies focused on comparing all pairs of submitted codes by using attribute counting, token pattern, program parse tree, and similarity measuring algorithm. It is important to provide a clear-cut model for distinguishing plagiarism and collaboration. This paper proposes a source code clustering algorithm using a probability model on extreme value distribution. First, we propose an asymmetric distance measure pdist($P_a$, $P_b$) to measure the similarity of $P_a$ and $P_b$ Then, we construct the Plagiarism Direction Graph (PDG) for a given program set using pdist($P_a$, $P_b$) as edge weights. And, we transform the PDG into a Gumbel Distance Graph (GDG) model, since we found that the pdist($P_a$, $P_b$) score distribution is similar to a well-known Gumbel distribution. Second, we newly define pseudo-plagiarism which is a sort of virtual plagiarism forced by a very strong functional requirement in the specification. We conducted experiments with 18 groups of programs (more than 700 source codes) collected from the ICPC (International Collegiate Programming Contest) and KOI (Korean Olympiad for Informatics) programming contests. The experiments showed that most plagiarized codes could be detected with high sensitivity and that our algorithm successfully separated real plagiarism from pseudo plagiarism.