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.

In order to keep up with the massive growing and expanding development of the electronics and informatics fields of study, the education system should be able to react on the currently used technologies available on the market and include them into the education process. Meeting the new trends becomes crucial when producing the people with broad view, experience and insight. Nowadays, Arduino based technologies are gaining huge popularity among the communities interested in fields of electronic and hardware solutions, because of its availability on the market, relatively easy use and last but not least, because of its cheap price and large accessibility of various information, components and already made tutorials. Therefore it becomes more and more crucial to include Arduino into education process within the subjects taught on schools, which would like to provide the complex view of electronics and informatics connected fields of study.

The main aim of the presented paper is to create a web portal, which allows the user to reckognize the sequence of chords in the song. To recognize chords from an audio recording, it is necessary to use input signal processing methods such as constant Q-transform or harmonic pitch class profile. A categorization template is created for each supported chord that maximizes the match between the processed signal and the chord. Techniques such as temporal and spectral smoothing or detection of harmonic changes were used for preprocessing. Based on the evaluation methods, the best classification strategy was selected, whose success prediction accuracy reaches 75 percent. Success has been achieved through the use of machine learning techniques. The system is publicly available on the public domain for anyone interested in this type of service.