STATISTICAL SOFTWARE R IN CORPUS-DRIVEN RESEARCH AND MACHINE LEARNING

The rapid development of computer software and network technologies has facilitated the intensive application of specialized statistical software not only in the traditional information technology spheres (i.e., statistics, engineering, artificial intelligence) but also in linguistics. The statistical software R is one of the most popular analytical tools for statistical processing a huge array of digitalized language data, especially in quantitative corpus linguistic studies of Western Europe and North America. This article discusses the functionality of the software package R, focusing on its advantages in performing complex statistical analyses of linguistic data in corpus-driven studies and creating linguistic classifiers in machine learning. With this in mind, a three-stage strategy of computer-statistical analysis of linguistic corpus data is elaborated: 1) data processing and preparing to be subjected to a statistical procedure, 2) utilizing statistical hypothesis testing methods (MANOVA, ANOVA) and the Tukey post-hoc test, and 3) developing a model of a linguistic classifier and analyzing its effectiveness. The strategy is implemented on 11 000 tokens of English detached nonfinite constructions with an explicit subject extracted from the BNC-BYU corpus. The statistical analysis indicates significant differences in the realization of the factors of the parameter “Part of speech of the subject”. The analyzed linguistic data are employed to build a machine model for the classification of the given constructions. Particular attention is devoted to the methodological perspectives of interdisciplinary research in the fields of linguistics and computer studies. The potential application of the elaborated case study in training undergraduate, master, and postgraduate students of Applied Linguistics is indicated. The article provides all the statistical data and codes written in the R script with comprehensive descriptions and explanations. The concluding part of the article summarizes the obtained results and highlights the issues for further research connected with the popularization of the statistical software complex R and raising the awareness of specialists in this statistical analysis system.

Reliable hypotheses testing in animal social network analyses: global index, index of interactions and residual regression

Animal social network analyses (ASNA) have led to a foundational shift in our understanding of animal sociality that transcends the disciplinary boundaries of genetics, spatial movements, epidemiology, information transmission, evolution, species assemblages and conservation. However, some analytical protocols (i.e., permutation tests) used in ASNA have recently been called into question due to the unacceptable rates of false negatives (type I error) and false positives (type II error) they generate in statistical hypothesis testing. Here, we show that these rates are related to the way in which observation heterogeneity is accounted for in association indices. To solve this issue, we propose a method termed the "global index" (GI) that consists of computing the average of individual associations indices per unit of time. In addition, we developed an "index of interactions" (II) that allows the use of the GI approach for directed behaviours. Our simulations show that GI: 1) returns more reasonable rates of false negatives and positives, with or without observational biases in the collected data, 2) can be applied to both directed and undirected behaviours, 3) can be applied to focal sampling, scan sampling or "gambit of the group" data collection protocols, and 4) can be applied to first- and second-order social network measures. Finally, we provide a method to control for non-social biological confounding factors using linear regression residuals. By providing a reliable approach for a wide range of scenarios, we propose a novel methodology in ASNA with the aim of better understanding social interactions from a mechanistic, ecological and evolutionary perspective.

Improved resolution of recalcitrant nodes in the animal phylogeny through the analysis of genome gene content and morphology

An accurate phylogeny of animals is needed to clarify their evolution, ecology, and impact on shaping the biosphere. Although multi-gene alignments of up to several hundred thousand amino acids are nowadays routinely used to test hypotheses of animal relationships, some nodes towards the root of the animal phylogeny are proving hard to resolve. While the relationships of the non-bilaterian lineages, primarily sponges (Porifera) and comb jellies (Ctenophora), have received much attention since more than a decade, controversies about the phylogenetic position of the worm-like bilaterian lineage Xenacoelomorpha and the monophyly of the "Superphylum" Deuterostomia have more recently emerged. Here we independently analyse novel genome gene content and morphological datasets to assess patterns of phylogenetic congruence with previous amino-acid derived phylogenetic hypotheses. Using statistical hypothesis testing, we show that both our datasets very strongly support sponges as the sister group of all the other animals, Xenoacoelomorpha as the sister group of the other Bilateria, and largely support monophyletic Deuterostomia. Based on these results, we conclude that the last common animal ancestor may have been a simple, filter-feeding organism without a nervous system and muscles, while the last common ancestor of Bilateria might have been a small, acoelomate-like worm without a through gut.

Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

<p>"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible."</p><p><br></p><p>Physiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.</p>

Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

<p>"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible."</p><p><br></p><p>Physiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.</p>

GOVERNMENT EFFORTS TO IMPLEMENT THE GREEN BUILDINGS MARKET IN DKI JAKARTA

Green buildings are very important and become a necessity that is environmentally friendly, this research was conducted to analyze the government's efforts to implement the market with buildings through aspects that influence it such as intellectual capital (human capital, organizational capital, customer capital) and governance. Sampling was carried out randomly through the distribution of questionnaires carried out in a number of markets spread across the DKI Jakarta area as a population, so that the results of research observations obtained a number of 170 samples filled in by market managers from different regions. Multiple linear regression and statistical hypothesis testing t are analyzes used to test hypotheses with data analysis tools using SPSS software. The research concludes that the dimensions of intellectual capital, namely human capital and organizational capital, have a significant positive effect, while customer capital and governance do not have a significant effect on market implementation with green buildings. The limitation of the study is that the sample used at random has not been able to conclude the overall results, In addition, the questionnaire submitted has the potential to be inconsistent with existing facts because of the possibility of subjectivity that can occur.

PENGARUH LINGKUNGAN KERJA DAN MOTIVASI TERHADAP KINERJA PEGAWAI DI KANTOR KECAMATAN WALANTAKA KOTA SERANG

The Walantaka District Office is one of the sub-districts in the city of Serang. Quality human resources are very important to achieve the goal. In addition, the work environment and motivation are important for agencies to achieve organizational goals. The purpose of this study was to determine the effect of work environment and motivation on employee performance. This type of research uses quantitative. The population of Walantak sub-district employees. The sample used is the saturated sample technique. The sample in this study were all employees of the Walantaka sub-district totaling 30 people. Data processing using SPSS 26 software. Hypothesis testing was carried out by descriptive statistical tests, validity and reliability tests, classical assumption tests, multiple linear regression tests, correlation coefficient analysis, coefficient of determination analysis and statistical hypothesis testing T test and F test. The results obtained from this study that the work environment affects employee performance while motivation does not affect employee performance, and simultaneously work environment and motivation affect employee performance

Analysis of distribution laws of transformer oil indicators in 110-330 kV transformers

Introduction. Ensuring the operational reliability of power transformers is an urgent task for the power industry in Ukraine and for most foreign countries. One of the ways to solve this problem is the correction of maximum permissible values of insulation parameters. However, such a correction is fundamentally impossible without an analysis of the laws of distribution of diagnostic indicators in the equipment with different states. The purpose of the research is to analyse the laws of distribution of the quality indicators of transformer oil with different states in 110 and 330 kV transformers. Novelty. It was found that both 330 kV autotransformers and 110 kV transformers have the displacements between the mathematical expectations of the distribution density of usable oil indicators. It caused by different service life of the analysed transformers and different values of load factors. This indicates the need to consider the influence of these factors when correcting the maximum permissible values of oil indicators. Also, the presence of displacement between the distribution densities of some indicators of usable oil in 110 kV transformers and 330 kV autotransformers has been revealed. It indicates a different intensity of oxidation reactions in transformers with different voltage class. In order to reduce the heterogeneity of initial data the procedure of statistical processing of in-service test results has been proposed as a method. This procedure combines the use of a priori information about the service life of equipment and values of load factors with the elements of statistical hypothesis testing. The results of the analysis of the distribution laws of transformer oil indicators with different states have shown that for both usable and unusable oil the values of oil indicators obey the Weibull distribution. Values of the shape and scale parameters for each of the obtained indices arrays have been obtained, as well as calculated and critical values of the goodness-of-fit criteria. Practical value. Obtained values of the distribution law parameters of the transformer oil indicators with different states, considering the service life and operating conditions allow to perform the correction of the maximum permissible values of the indicators using the statistical decision-making methods.