Vol 21, No 5 (2022)

An attention-based random survival forest (Att-RSF) is presented in the paper. The first main idea behind this model is to adapt the Nadaraya-Watson kernel regression to the random survival forest so that the regression weights or kernels can be regarded as trainable attention weights under important condition that predictions of the random survival forest are represented in the form of functions, for example, the survival function and the cumulative hazard function. Each trainable weight assigned to a tree and a training or testing example is defined by two factors: by the ability of corresponding tree to predict and by the peculiarity of an example which falls into a leaf of the tree. The second main idea behind Att-RSF is to apply the Huber's contamination model to represent the attention weights as the linear function of the trainable attention parameters. The Harrell's C-index (concordance index) measuring the prediction quality of the random survival forest is used to form the loss function for training the attention weights. The C-index jointly with the contamination model lead to the standard quadratic optimization problem for computing the weights, which has many simple algorithms for its solution. Numerical experiments with real datasets containing survival data illustrate Att-RSF.

State-of-the-art models of artificial intelligence are developed in the black-box paradigm, in which sensitive information is limited to input-output interfaces, while internal representations are not interpretable. The resulting algorithms lack explainability and transparency, requested for responsible application. This paper addresses the problem by a method for finding Osgood’s dimensions of affective meaning in multidimensional space of a pre-trained word2vec model of natural language. Three affective dimensions are found based on eight semantic prototypes, composed of individual words. Evaluation axis is found in 300-dimensional word2vec space as a difference between positive and negative prototypes. Potency and activity axes are defined from six process-semantic prototypes (perception, analysis, planning, action, progress, and evaluation), representing phases of a generalized circular process in that plane. All dimensions are found in simple analytical form, not requiring additional training. Dimensions are nearly orthogonal, as expected for independent semantic factors. Osgood’s semantics of any word2vec object is then retrieved by a simple projection of the corresponding vector to the identified dimensions. The developed approach opens the possibility for interpreting the inside of black box-type algorithms in natural affective-semantic categories, and provides insights into foundational principles of distributive vector models of natural language. In the reverse direction, the established mapping opens machine-learning models as rich sources of data for cognitive-behavioral research and technology.

Deep learning has been instrumental in solving difficult problems by automatically learning, from sample data, the rules (algorithms) that map an input to its respective output. Purpose: Perform land use landcover (LULC) classification using the training data of satellite imagery for Moscow region and compare the accuracy attained from different models. Methods: The accuracy attained for LULC classification using deep learning algorithm and satellite imagery data is dependent on both the model and the training dataset used. We have used state-of-the-art deep learning models and transfer learning, together with dataset appropriate for the models. Different methods were applied to fine tuning the models with different parameters and preparing the right dataset for training, including using data augmentation. Results: Four models of deep learning from Residual Network (ResNet) and Visual Geometry Group (VGG) namely: ResNet50, ResNet152, VGG16 and VGG19 has been used with transfer learning. Further training of the models is performed with training data collected from Sentinel-2 for the Moscow region and it is found that ResNet50 has given the highest accuracy for LULC classification for this region. Practical relevance: We have developed code that train the 4 models and make classification of the input image patches into one of the 10 classes (Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, and Sea&Lake).

The increasing flow of photo and video information transmitted through the channels of infocommunication systems and complexes stimulates the search for effective compression algorithms that can significantly reduce the volume of transmitted traffic, while maintaining its quality. In the general case, the compression algorithms are based on the operations of converting the correlated brightness values of the pixels of the image matrix into their uncorrelated parameters, followed by encoding the obtained conversion coefficients. Since the main known decorrelating transformations are quasi-optimal, the task of finding transformations that take into account changes in the statistical characteristics of compressed video data is still relevant. These circumstances determined the direction of the study, related to the analysis of the decorrelating properties of the generated wavelet coefficients obtained as a result of multi-scale image transformation. The main result of the study was to establish the fact that the wavelet coefficients of the multi-scale transformation have the structure of nested matrices defined as submatrices. Therefore, it is advisable to carry out the correlation analysis of the wavelet transformation coefficients separately for the elements of each submatrix at each level of decomposition (decomposition). The main theoretical result is the proof that the core of each subsequent level of the multi-scale transformation is a matrix consisting of the wavelet coefficients of the previous level of decomposition. It is this fact that makes it possible to draw a conclusion about the dependence of the corresponding elements of neighboring levels. In addition, it has been found that there is a linear relationship between the wavelet coefficients within the local area of ​​the image with a size of 8×8 pixels. In this case, the maximum correlation of submatrix elements is directly determined by the form of their representation, and is observed between neighboring elements located, respectively, in a row, column or diagonally, which is confirmed by the nature of the scattering. The obtained results were confirmed by the analysis of samples from more than two hundred typical images. At the same time, it is substantiated that between the low-frequency wavelet coefficients of the multi-scale transformation of the upper level of the expansion, approximately the same dependences are preserved uniformly in all directions. The practical significance of the study is determined by the fact that all the results obtained in the course of its implementation confirm the presence of characteristic dependencies between the wavelet transform coefficients at different levels of image decomposition. This fact indicates the possibility of achieving higher compression ratios of video data in the course of their encoding. The authors associate further research with the development of a mathematical model for adaptive arithmetic coding of video data and images, which takes into account the correlation properties of wavelet coefficients of a multi-scale transformation.

The paper considers the problem of a wireless communication system’s physical level security for a multipath signal propagation channel and the presence of a wiretap channel. To generalize the propagation effects, the Beaulieu-Xie shadowed channel model was assumed. To describe the security of the information transfer process, such a metric as the secure outage probability of was considered. An analytical expression of the secure outage probability was obtained and analyzed depending on the characteristics of the channel and the communication system: the average value of the signal-to-noise ratio in the main channel and the wiretap channel, the effective path-loss exponent, the relative distance between the legitimate receiver and the wiretap and the threshold bandwidth, normalized to the bandwidth of a smooth Gaussian channel. The analysis considers the sets of parameters that cover all practically important scenarios for the functioning of a wireless communication systems: both deep fading (corresponding to the hyper-Rayleigh scenario) and small fading, both in the case of a significant line-of-sight component and a significant number of multipath clusters, and with significant shadowing of the dominant component and a small number of multipath waves, as well as all intermediate options. It is found out that the value of the energy requirements for guaranteed secure communication at a given speed is determined primarily by the power of multipath components, as well as the existence of an irreducible secure outage probability of a communication session with an increase for channels with strong overall shadowing of the signal components, which from a practical point of view is important to take into account when imposing requirements for the values of the signal-to-noise ratio and the data transfer rate in the direct channel, providing the desired degree of security of the wireless communication session.