编号 1 (2025)
AI-enabled Systems
Attack and Anomaly Detection in Containerized Systems: Signature and Rule-Based Approaches
摘要
The article considers one of the key problems of container systems related to the detection of attacks and anomalies. The mechanisms of isolation of container systems and attacks on such systems are described. A classification of approaches to the detection of attacks and anomalies is presented. A systematic analysis of the main approaches to the detection of attacks and anomalies in container systems, as well as methods for their implementation, is performed. Traditional approaches based on signatures and rules, their features, advantages and disadvantages are considered in detail.
3-13
Decomposition Method of Functional Characteristics of Artificial Intelligence Systems
摘要
In making decisions on the possibility of using artificial intelligence systems to solve specific applications, one of the critical problems is an objective assessment of the functional characteristics for these systems in the planned operating conditions. The article proposes a universal approach to justifying the composition of environmental factors, the variability of which should be taken into account to ensure the required level of representativeness of the conducted tests of artificial intelligence systems. The proposed approach is based on the assumption that it is necessary to develop a classifier of elementary intellectual functions similar to the functional structure of human intelligence and having certain, unified within one class, requirements to the variability of their testing conditions.
14-32
Intelligent Systems and Robots
Motion Classification by Artificial Neural Network for Bionic Hand Control
摘要
The results of training and testing of an artificial neural network for recognizing human finger movements based on signals from electromyographic sensors are presented. Special attention is paid to the issues of preliminary processing of initial signals, including digital filtering, setting the optimal level corresponding to the resting state of the muscle, and calculation of signal attributes. In the paper, an envelope of the electromyographic signal was built on the basis of the “average energy” attribute, and the definition of muscle activity areas was carried out using two thresholds: adaptive in level and fixed in time. Three attributes are used directly for training the artificial neural network, which are specified depending on the requirements to the quality of training, either by indicator of distinguishability or by a complete enumeration of combinations of attributes. Optimization of the set of attributes for training the artificial neural network allowed achieving the level of correct answers more than 97%.
33-45
Optimal and Rational Choice
An Algorithm for Selecting Linear Regression Features to Solve the Multicollinearity Problem
摘要
The paper considers the problem of selecting linear regression factors using an optimization model that includes characteristics of the relationship of features, as well as the dependence of the feature and the effective indicator. To solve it, it is proposed to reformulate the original problem in the form of an inverse while minimizing the sum of the absolute values of the arguments. The results of computational experiments, including comparison with nonlinear programming methods implemented in mathematical packages and the Python library, demonstrated the high efficiency of the proposed algorithm for solving the modified problem.
46-55
Machine Learning, Neural Networks
On the Properties of the Limit Set of a Repeated Machine Learning Process under Feature Space Transformations
摘要
Widely used in practice the recommender systems, decision support systems, intelligent control, AI assistants in medicine, and search engines can influence users and properties of the environment in which they are employed. The process of repeated machine learning describes such systems in which continuous improvement of machine learning models is performed over time using training data obtained from the users. In this paper, we study how feature space transformations influence properties of the repeated machine learning process. In particular, we investigate the conditions under which the prediction of the asymptotic behavior of a system over time obtained in the original space can be applied to a similar system in the transformed space. The results of the research indicate the possibility of using simpler systems in spaces of lower dimensionality to study processes in more complex systems.
56-66
Data Preprocessing for Building a Neural Network Model to Predict the State of a Technical Object
摘要
In order to prevent emergency situations in the operation of a technical facility, it is necessary to predict its state. To solve this problem, neural network models are used in the work. However, for effective training of models and obtaining more accurate forecasting results, the input data should be preprocessed. In this paper, a new technique is proposed for preprocessing the initial data when constructing neural network models, which includes algorithms for finding outliers, restoring missing values, and removing correlating factors. A special program in the Python programming language was written to implement the proposed technique. The study of the effectiveness of the proposed data preprocessing technique for predicting the state of a technical facility was carried out using two objects as an example: a turbojet engine and a lithium-ion battery. The following approaches were used to compare the results: the data preprocessing technique from the AutoKeras library and the method based on the use of a compactness profile. It is shown that the use of the proposed data preprocessing approach increases the forecasting accuracy of neural network models by approximately 3–4 times compared to the other two approaches.
67-81
Analysis of Textual and Graphical Information
Automatic Lexical Adaptation of Russian Texts
摘要
The article describes a method for lexical simplification of Russian text, using a specially marked base of synonyms and a set of rules, which allows automatic lexical replacement of words and phrases with restoration of the correct syntax and preservation of the text semantics. To form a marked database of synonyms, dictionaries of synonyms that are in the public domain were used. To preserve semantics, an analysis and reduction of synonymous series was carried out, the frequency of members of the synonymous series was analyzed in order to select a dominant, and entries in dictionaries were marked and a mechanism for processing labels was proposed to comply with the syntax rules in a simplified text. A base of production rules has been developed to preserve the correct syntax after lexical adaptation of the text, allowing for the correct replacement of individual words, phrases with one word and phrases with a phrase.
82-94
The Impact of Hierarchical Discourse Features on Coreference Resolution in Russian
摘要
This study investigates the role of hierarchical discourse features in coreference resolution within Russian texts. It evaluates the effectiveness of rhetorical parsers in handling coreference across texts of varying genres and lengths. The paper also identifies key characteristics of rhetorical structure annotation corpora that influence the quality of coreference resolution in diverse linguistic contexts.
95-102
Analysis of Signals, Audio and Video Information
Application of Machine Learning Methods to Image Analysis of Chronic Wounds
摘要
Neural networks and deep learning algorithms are increasingly used in medicine, including image analysis. In surgery, soft tissue wounds assessment remains challenging but necessary issue to assess the course of healing process and treatment effectiveness. Digital wound images are used for noncontact wound analysis. The paper presents the results of pre-trained network models (AlexNet, ResNet50, ResNet152, VGG16) used to classify pressure ulcer images as examples of chronic wounds. The Segment Anything Model (SAM) demonstrated an accuracy of 86.46% in solving the problem of segmenting the edges of a wound defect and tissue types within it. The results can be used to create an expert system for analyzing soft tissue wound images.
103-114
Two Methods for Estimating Optical Flow from a Video Sequence of Images
摘要
The content of the article focuses on calculating optical flow from a series of images. Two techniques are presented for solving this challenging computational problem. These techniques play a crucial role in various fields of computer vision, including object tracking, scene analysis, microand macro-motion detection, facial expression recognition, and more. Both techniques complement each other: the first technique, which uses fast convolutions, is best for calculating the video stream across all pixels in an image; the second technique, which relies on robust estimates for linear regression parameters, is better suited for point configurations. It is recommended to perform pre-processing with the first technique to minimize contrast effects, whereas the image quality has little impact on the results with the second technique due to its robust estimates. By their nature, these methods are related to variational approaches for calculating optical flow. However, they differ significantly from the methods described in the literature in terms of speed and accuracy. These methods do not require the use of deep learning, so they can be applied without a large training dataset for methods that utilize deep neural networks for optical flow computation. The results obtained on grayscale images can easily be extended to color images, and most importantly, to systems of secondary features that have recently been used in computer vision.
115-127