Financial globalization: manna or menace? The case of Mexican banking

Banks and banking - Mexico

Mexico emerges from 10-year credit slump

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