Gender Equality and Institutions as the Driving Force of Football Performance: Women vs Men

Gender equality should be a necessity in every developed economy of the world. Despite this assumption, this is not the case. The field of sports is no exception. This study addresses the relationship between gender equality, institutions and football performance of national teams. Correlation and regression analysis is used to determine the relationship between variables. The results suggest that higher gender equality leads to better performance for footballers on the fields. Countries with higher gender equality perform better (more FIFA points). The economic condition of the country has a similar effect on performance. Estimates have shown a statistically significant positive relationship between economic prosperity and performance on the pitch. Climate and age of players do not affect the performance of national teams. Institutional factors significantly affect players’ performance. Members of the European Union perform significantly higher than those that are not in the EU. As well as countries in which there was no communist regime in the past . Keywords: gender inequality index, FIFA ranking, men, women, institutions

Multi-criteria approach to selecting sites for solar-diesel hybrid systems

The paper aims to develop a site selection procedure for solar-diesel hybrid systems using a multi-criteria performance analysis of site options. The site selection process using this multi-criteria approach was carried out on the example of Kungurtug rural settlement (Tyva Republic). The area surrounding this settlement was analyzed, revealing four possible sites for a solar-diesel system. For evaluating the performance of these site options, the following criteria were adopted: ease of installation and maintenance of the solar-diesel hybrid system; surface topography and soil quality; convenience of the photovoltaic cell layout; environmental impact of the solar-diesel hybrid system; opportunities for further expansion of the system; orientation potential of the photovoltaic cell. In order to assess the significance of the concordance coefficient, the distribution quantile was determined, amounting to 16.2. For 5 degrees of freedom and a significance level of 0.05, the table value of the concordance coefficient amounted to 16.2. Since the distribution quantile is greater than the table value, the concordance coefficient can be considered significant (95% confidence level), indicating agreement between expert opinions. Experts ranked the site options to obtain relative performance estimates for the criteria; numeric indicators were converted into the relative estimates using linear transformation formulas. The multi-criteria performance estimates of the possible options were calculated for arithmetic mean and harmonic convolutions. After comparing the site options for the solar-diesel system, the second variant characterized by the highest criterion scores was selected for Kungurtug settlement. As a result, a site selection procedure for the elements of solar-diesel hybrid systems was developed using the theory of multi-criteria optimization and the method of expert evaluations, allowing a set of technical, economic, climatic, and environmental criteria to be taken into account.

Low self-esteem and the formation of global self-performance estimates in emerging adulthood

High self-esteem, an overall positive evaluation of self-worth, is a cornerstone of mental health. Previously we showed that people with low self-esteem differentially construct beliefs about momentary self-worth derived from social feedback. However, it remains unknown whether these anomalies extend to constructing beliefs about self-performance in a non-social context, in the absence of external feedback. Here, we examined this question using a novel behavioral paradigm probing subjects’ self-performance estimates with or without external feedback. We analyzed data from young adults (N=57) who were selected from a larger community sample (N=2,402) on the basis of occupying the bottom or top 10% of a reported self-esteem distribution. Participants performed a series of short blocks involving two perceptual decision-making tasks with varying degrees of difficulty, with or without feedback. At the end of each block, they had to decide on which task they thought they performed best, and gave subjective task ratings, providing two measures of self-performance estimates. We found no robust evidence of differences in objective performance between high and low self-esteem participants. Nevertheless, low self-esteem participants consistently underestimated their performance as expressed in lower subjective task ratings. These results provide an initial window onto how cognitive processes underpinning the construction of self-performance estimates across different contexts map on to global dispositions relevant to mental health, such as self-esteem.

A-1 The Influence of Base Rate and Sample Size on Performance of a Random Forest Classifier for Dementia Prediction: Implications for Recruitment

Objective Machine learning (ML) classifier performance estimates are affected by sample size and class imbalance in training data, and yet performance is often reported with balanced data. We explore the effect of varying sample size and dementia conversion base rate on the performance of a classifier that predicts future dementia. Method Longitudinal data from the National Alzheimer’s Coordination Center (NACC) Uniform Data Set (UDS) were used. All participants had MCI at baseline. A random forest classifier (RFC) was trained to predict dementia at 1, 2, and 3 years. Predictors included baseline neuropsychological test scores, demographics, and health history. Cases were sampled at multiple sample sizes (N = 125, 250, 500, 1000 and 2000) and base rates (0.1, 0.2, 0.3, 0.4, and 0.5). Performance was evaluated using Matthews Correlation Coefficient (MCC). Results For balanced data (N = 1000), the classifier predicts conversion to dementia at 3 years with an MCC of 0.54 (sensitivity = 0.79; specificity = 0.75). As expected, means of classifier performance estimates decline as the conversion rate decreases. Likewise, variability of estimates increases with smaller sample sizes. For a conversion rate of 30%, consistent with many memory clinics, classifier performance declines only moderately (MCC = 0.44). In conversion rates of 10% and 20%, performance approaches chance. Performance trends illustrated in Figure 1. Conclusions Such classifiers may have clinical utility in memory clinics with higher conversion rates. Expected tradeoffs are observed with respect to diminishing sample size increasing error variance, and higher base rates of positive cases improving overall performance. Results provide potential guidelines for sample size and recruitment targets with RFC designs.

DiLizium: A Two-Party Lattice-Based Signature Scheme

In this paper, we propose DiLizium: a new lattice-based two-party signature scheme. Our scheme is constructed from a variant of the Crystals-Dilithium post-quantum signature scheme. This allows for more efficient two-party implementation compared with the original but still derives its post-quantum security directly from the Module Learning With Errors and Module Short Integer Solution problems. We discuss our design rationale, describe the protocol in full detail, and provide performance estimates and a comparison with previous schemes. We also provide a security proof for the two-party signature computation protocol against a classical adversary. Extending this proof to a quantum adversary is subject to future studies. However, our scheme is secure against a quantum attacker who has access to just the public key and not the two-party signature creation protocol.

Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging

As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare different algorithms. While the machine learning community has generally accepted methods such as k-fold stratified cross-validation (CV) to be more rigorous than single split validation, the standard research practice in medical fields is the use of single split validation techniques. This is especially concerning given the relatively small sample sizes of datasets used for cardiovascular imaging. We aim to examine how train-test split variation impacts the stability of machine learning (ML) model performance estimates in several validation techniques on two real-world cardiovascular imaging datasets: stratified split-sample validation (70/30 and 50/50 train-test splits), tenfold stratified CV, 10 × repeated tenfold stratified CV, bootstrapping (500 × repeated), and leave one out (LOO) validation. We demonstrate that split validation methods lead to the highest range in AUC and statistically significant differences in ROC curves, unlike the other aforementioned approaches. When building predictive models on relatively small data sets as is often the case in medical imaging, split-sample validation techniques can produce instability in performance estimates with variations in range over 0.15 in the AUC values, and thus any of the alternate validation methods are recommended.

An Enhanced CNN-2D for Audio-Visual Emotion Recognition (AVER) Using ADAM Optimizer

The importance of integrating visual components into the speech recognition process for improving robustness has been identified by recent developments in audio visual emotion recognition (AVER). Visual characteristics have a strong potential to boost the accuracy of current techniques for speech recognition and have become increasingly important when modelling speech recognizers. CNN is very good to work with images. An audio file can be converted into image file like a spectrogram with good frequency to extract hidden knowledge. This paper provides a method for emotional expression recognition using Spectrograms and CNN-2D. Spectrograms formed from the signals of speech it’s a CNN-2D input. The proposed model, which consists of three layers of CNN and those are convolution layers, pooling layers and fully connected layers extract discriminatory characteristics from the representations of spectrograms and for the seven feelings, performance estimates. This article compares the output with the existing SER using audio files and CNN. The accuracy is improved by 6.5% when CNN-2D is used.

Inherent Parallelism and Speedup Estimation of Sequential Programs

Although several automated Parallel Conversion solutions are available, very few have attempted, to provide proper estimates of the available Inherent Parallelism and expected Parallel Speedup. CALIPER which is the outcome of this research work is a parallel performance estimation technology that can fill this void. High level language structures such as Functions, Loops, Conditions, etc which ease program development, can be a hindrance for effective performance analysis. We refer to these program structures as the Program Shape. As a preparatory step, CALIPER attempts to remove these shape related hindrances, an activity we refer to as Program Shape Flattening. Programs are also characterized by dependences that exist between different instructions and impose an upper limit on the parallel conversion gains. For parallel estimation, we first group instructions that share dependences, and add them to a class we refer to as Dependence Class or Parallel Class. While instructions belonging to a class run sequentially, the classes themselves run in parallel. Parallel runtime, is now the runtime of the class that runs the longest. We report performance estimates of parallel conversion as two metrics. The inherent parallelism in the program is reported, as Maximum Available Parallelism (MAP) and the speedup after conversion as Speedup After Parallelization (SAP).