The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allow reducing the number of errors in identifying users of information systems by voice by an average of 1,5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.

The paper discusses various ways of activating phosphorus slags by introducing additives for the development of phosphorus slag binders (PSB), replacing cement. Considering that pseudowollastonite is the main mineral of phosphorus slags and without activating components does not possess the binding properties necessary for the production of building materials based on them, we used compositions of small amounts of sodium hydroxide with alkali metal salts, the anions of which form poorly soluble compounds with calcium. When choosing activating components, scarce alkaline additives were replaced by waste from chemical plants, which allows a passing solution of their practical use and environmental problems. The strength at a sodium hydroxide content of 1 - 4 % after curing of slag samples of various batches was in the range of 50,0 - 70,0 MPa. Samples of binders of normal hardening at the age of 28 days with a sodium hydroxide content of 0,5; 1,0, 2 and 4 % had the strength of 20,3; 35,4; 45,6; 55,8 MPa, respectively. The effect of the combined presence of alkali and salt is especially noticeable for small amounts of sodium hydroxide. Binders containing a composition of cement with salts under normal conditions and after curing showed a slightly lower strength than in an alkaline medium. With a constant cement content (4 %), the strength indicators increase with an increase in the proportion of the salt additive, reaching at 4 % its maximum value. The effect of the nature of activators on pH was determined. The data obtained indicate the advantages of using PSB and various industrial wastes with a low content of alkaline compounds in the production.

Ensuring the best quality and performance of modern speech technologies, today, is possible based on the widespread use of machine learning methods. The idea of this project is to study and implement an end-to-end system of automatic speech recognition using machine learning methods, as well as to develop new mathematical models and algorithms for solving the problem of automatic speech recognition for agglutinative (Turkic) languages. Many research papers have shown that deep learning methods make it easier to train automatic speech recognition systems that use an end-to-end approach. This method can also train an automatic speech recognition system directly, that is, without manual work with raw signals. Despite the good recognition quality, this model has some drawbacks. These disadvantages are based on the need for a large amount of data for training. This is a serious problem for low-data languages, especially Turkic languages such as Kazakh and Azerbaijani. To solve this problem, various methods are needed to apply. Some methods are used for end-to-end speech recognition of languages belonging to the group of languages of the same family (agglutinative languages). Method for low-resource languages is transfer learning, and for large resources - multi-task learning. To increase efficiency and quickly solve the problem associated with a limited resource, transfer learning was used for the end-to-end model. The transfer learning method helped to fit a model trained on the Kazakh dataset to the Azerbaijani dataset. Thereby, two language corpora were trained simultaneously. Conducted experiments with two corpora show that transfer learning can reduce the symbol error rate, phoneme error rate (PER), by 14,23 % compared to baseline models (DNN + HMM, WaveNet, and CNC + LM). Therefore, the realized model with the transfer method can be used to recognize other low-resource languages.