ÐWe have found that, when software is developed by multiple, geographically separated teams, the cost-benefit trade-offs of software inspection change. In particular, this situation can significantly lengthen the inspection interval (calendar time needed to complete an inspection). Our research goal was to find a way to reduce the inspection interval without reducing inspection effectiveness. W...

Interval scale grouped data have peculiar structures of their own rights among various archetypes of polytomous data that deserve special statistical treatments. Maximum likelihood type approaches along with heteroscedastic and transformation models are adapted to take into account this kind of architecture with current state-of-art computation capabilities. Meanwhile, misclassification rates i...

Our paper focuses on a robustness analysis of efficiency scores in the context Data Envelopment Analysis (DEA) assuming interval scale data, as defined A. Dehnokhalaji, P. J. Korhonen, M. Köksalan, N. Nasrabadi and Wallenius, “Efficiency to incorporate data”, European Journal Operational Research 207 (2), 2010, pp. 1116–1121. We first show that definition score used our is well-defined measure ...

Human observers readily make judgements about the degree of order in planar arrangements of points (point patterns). Here, based on pairwise ranking of 20 point patterns by degree of order, we have been able to show that judgements of order are highly consistent across individuals and the dimension of order has an interval scale structure spanning roughly 10 just-notable-differences (jnd) betwe...

Abstract When we have a digital image which contains some objects and a ruler, we know the size of objects by rough estimate. Since measuring the object size in the digital image is usually manual, we developed the detection method of scale interval on a ruler. In our method we transfer digital image into frequency space using two-dimensional discreet fourier transformation, a specific frequenc...

envelopment analysis (dea) is a very eective method to evaluate the relative eciency of decision-making units (dmus). dea models divided all dmus in two categories: ecient and inecientdmus, and don't able to discriminant between ecient dmus. on the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ...