A monitoring platform for distributed Java applications

This paper presents a new Java oriented monitoring infrastructure that enables tools to observe, analyze and manipulate the execution of distributed Java applications independent of implementation details like instrumentation of monitored entities, hardware platform and application libraries. Tools can access the monitored application via a standardized interface defined by an On-Line Monitoring Interface Specification (OMIS) and extended by a set of new Java-specific services relating to garbage collection, class loading, remote method invocation, etc. The new monitoring functionality can be applied for building various kinds of tools and for adapting the already existing ones, such as performance analyzers, debuggers, etc., working in the on-line mode

Evaluating the progeny of European beech (Fagus sylvatica L.) in the early years of growth

This research was carried out on two experimental plots located in the Rymanów and Nawojowa forest districts. In the second and fifth year after planting, at three and six years of age respectively, survival and height of 25 beech progenies of selected stands were measured. Furthermore, we show the effect of beech origin and growth environment (significant ‘provenance × block’ and ‘provenance × test plot’ interactions). Beeches from both experimental plots differed significantly in growth and survival and this difference increased with tree age. The highest provenance heritability was obtained for the tree height after two years of growth in Rymanów. In Nawojowa, the heritability of beech survival reached zero after five years of growth. An evaluation of the stability of beech provenances (genotypes) in terms of survival and height under the habitat conditions of our experimental plots was done using the Finlay and Wilkinson method. The beech provenances of 469–Nawojowa and 452–Lesko (regional standard) were included as a stable basis for reference. A high degree of stability and high average values for the characteristics investigated indicate high progeny quality within these stands

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1 x N and N x N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms