This paper focuses on training machine learning models using the XGBoost and extremely randomized trees algorithms on two datasets obtained using static and dynamic analysis of real malicious and benign samples. We then compare their success rates—both mutually and with other algorithms, such as the random forest, the decision tree, the support vector machine, and the naïve Bayes algorithms, which we compared in our previous work on the same datasets. The best performing classification models, using the XGBoost algorithm, achieved 91.9% detection accuracy and 98.2% sensitivity, 0.853 AUC, and 0.949 F1 score on the static analysis dataset, and 96.4% accuracy and 98.5% sensitivity, 0.940 AUC, and 0.977 F1 score on the dynamic analysis dataset. Then, we exported the best performing machine learning models and used them in our proposed MLMD program, automating the process of static and dynamic analysis and allowing the trained models to be used for classification on new samples.

This paper reports on an investigation which is part of a comprehensive project aimed at investigating translators’ work-related happiness in various contexts, a subject largely under-researched in contemporary translation studies. The purpose of this pilot cohort study is to determine the perceptions of happiness in two presumably high-profile groups of translators—Slovak sworn translators and Slovak EU translators. To accomplish the aim, comparative and causal perspectives are used. The quantitative analysis, comprising descriptive and correlation analysis, involves data from questionnaires completed by a total of 115 translators belonging to the two groups (83 + 32). The respondents’ perceptions of their work-related happiness are examined and compared, based on their responses to questions revolving primarily around social status variables and parameters of occupational prestige. Based on the gained data, seven hypotheses are tested with quantitative research methods employing contingency tables. Although our findings largely do not corroborate the hypotheses and lead to the identification of crucial differences between the two groups, the analyses also allow us to identify some commonalities. The results of the quantitative analysis are discussed in detail.

The architecture described in this article belongs to a class of dynamic data flow architectures, in which the operand process control significantly affects the performance parameters as well as the system characteristics of the given architecture. The main component of the presented architecture is the multifunctional pipeline unit, which handles processing of data tokens representing the instructions of the program. This article describes the pipelined single operand data flow operators process control.