502 research outputs found
Financial globalization: manna or menace? The case of Mexican banking
Banks and banking - Mexico
October 4, 1962
https://scholarlycommons.obu.edu/arbaptnews/1243/thumbnail.jp
Tendencies in financing the agricultural and food sector under the common agricultural policy
Poland, having joined the EU, became subject to regulations that significantly changed the conditions under which its food industry functioned. As markets opened up to each other, the possibilities of sale increased and competitiveness of economic entities improved. Mobilised public funds contributed, among other things, to modernisation of agricultural holdings and food industry enterprises, improvement of their competitiveness, construction of the infrastructure and multifunctional development of rural areas. The paper discusses tendencies in financing agriculture under the Common Agricultural Policy in Poland against the production and economic situation of the agricultural and food sector
Series of Events on Tap to Celebrate Fitz\u27s Presidency
News release announces that the presidency of Brother Raymond L. Fitz, S.M., will be celebrated with several events
Variance of ML-based software fault predictors: are we really improving fault prediction?
Software quality assurance activities become increasingly difficult as
software systems become more and more complex and continuously grow in size.
Moreover, testing becomes even more expensive when dealing with large-scale
systems. Thus, to effectively allocate quality assurance resources, researchers
have proposed fault prediction (FP) which utilizes machine learning (ML) to
predict fault-prone code areas. However, ML algorithms typically make use of
stochastic elements to increase the prediction models' generalizability and
efficiency of the training process. These stochastic elements, also known as
nondeterminism-introducing (NI) factors, lead to variance in the training
process and as a result, lead to variance in prediction accuracy and training
time. This variance poses a challenge for reproducibility in research. More
importantly, while fault prediction models may have shown good performance in
the lab (e.g., often-times involving multiple runs and averaging outcomes),
high variance of results can pose the risk that these models show low
performance when applied in practice. In this work, we experimentally analyze
the variance of a state-of-the-art fault prediction approach. Our experimental
results indicate that NI factors can indeed cause considerable variance in the
fault prediction models' accuracy. We observed a maximum variance of 10.10% in
terms of the per-class accuracy metric. We thus, also discuss how to deal with
such variance
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