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Most multi-agent system (MAS) testing techniques lack empirical evidence of their effectiveness. Since finding tests that can reveal a large proportion of possible faults is a key goal in testing, we need techniques to assess the fault detection ability of test sets for MAS. Mutation testing offers a direct and powerful way to do this: it generates modified(More)
Dermatofibrosarcoma protuberans (DFSP) is a rare malignant tumor of subcutaneous tissue characterized by slow infiltrative growth. The tumor occurs in patients of all ages, with the highest frequency occurring between the second and the fifth decades of age. Genetically, DFSP is characterized by a reciprocal translocation t(17;22)(q22;q13), or more often,(More)
BACKGROUND & OBJECTIVE The prognosis of supraglottic squamous cell carcinoma (SGSCC) was poor. To authors' knowledge, the relationship between inducible nitric oxide synthase (iNOS) and basic fibroblast growth factor (bFGF) and the prognosis of SGSCC has not been addressed to date. This study was designed to evaluate the prognostic significance of these two(More)
Outlier detection is an important research problem in data mining and image analysis. In this paper, the ideas in the PageRank algorithm are borrowed to construct a novel outlier detection method. In this method, three detecting stages are performed to detect three different types of outliers by using different detecting strategies. The whole process is(More)
This paper introduces semantic mutation testing (SMT) into multiagent systems. SMT is a test assessment technique that makes changes to the interpretation of a program and then examines whether a given test set has the ability to detect each change to the original interpretation. These changes represent possible misunderstandings of how the program is(More)
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