High-Granular Micro-Segmentation in Campus Networks based on Downloadable Access Control Lists

Micro-segmentation of local networks is an important element of network security. The main goal of micro-segmentation of network is to reduce a risk of compromising hosts during a cyber-attack. In micro-segmented networks, if one of the hosts has been compromised, the malicious code or attacker will be limited in the "horizontal" actions by the micro-segment to which the compromised host belongs. Existing methods of micro-segmentation of networks have operational drawbacks that impede their effective practical application. This article presents a new method of micro-segmentation of local wired and wireless networks based on downloadable and wireless access control lists, which allows to achieve a high level of granularity of network access policies by minimizing the microsegment, along with high operational characteristics.

Software Architecture of an Automated Greenhouse Complex based on Cloud Technologies

To grow crops in greenhouse complexes, it is necessary to maintain the microclimatic conditions required for these crops and monitor them. Automation of these processes is an urgent task, and for its solution it is necessary to use both hardware and software. The paper proposes a cloud service architecture for managing robotic vertical farms, which allows for reliable two-way data transfer between the greenhouse modules and the operator and can be used for growing crops both in vertical hydroponic installations and in single-level greenhouses of greenhouses. The business processes are described that allow you to understand and evaluate the principles of organizing monitoring and management of cultivation. On the basis of the developed architecture, software has been implemented, which has been tested in the tasks of managing the greenhouse complex and obtaining data on the microclimate. Thanks to the use of wireless networks, the absence of restrictions on the type and location of modules, duplication of functionality and data replication, such a system can be considered flexible in use and scalable.

Flow Balancing in Modeling of Multi-Phase Flow using Non-Conformal Finite Element Meshes

The method of balancing numerical finite element flows in modeling a process of multiphase flow using non-conformal hexagonal meshes is considered. Studies have been carried out for a simple reservoir configuration and on a more complex model of a real field of high-viscosity oil in the Tatarstan. The research results showed that the balancing method allows one to obtain a conservative solution when using non-conformal finite element meshes with sufficiently large cells. At the same time, this method is completely free of problems associated with grid orientation, even for complex models containing zones with highly variable permeability. The proposed algorithm for the adaptive choice of parameters allows to do the factorization of the SLAE matrix at sufficiently small number of time steps; therefore, the computational costs of the flow balancing procedure are an order of magnitude less than the costs associated with calculating the pressure field and phase transfer. The used non-conformal finite element meshes with an arbitrary number of docked hexagons can significantly reduce the number of degrees of freedom when modeling multiphase flows in reservoirs with much small local heterogeneity and in the presence of several perforated zones. As a result, computational costs are reduced by almost an order of magnitude, and, at the same time, the required approximation accuracy is maintained. With an increase in the scale of the model and the number of operating wells, this advantage increases even more.

2D-to-3D Projection for Monocular and Multi-View 3D Multi-class Object Detection in Indoor Scenes

In this paper, we propose a novel method of joint 3D object detection and room layout estimation. The proposed method surpasses all existing methods of 3D object detection from monocular images on the indoor SUN RGB-D dataset. Moreover, the proposed method shows competitive results on the ScanNet dataset in multi-view mode. Both these datasets are collected in various residential, administrative, educational and industrial spaces, and altogether they cover almost all possible use cases. Moreover, we are the first to formulate and solve a problem of multi-class 3D object detection from multi-view inputs in indoor scenes. The proposed method can be integrated into the controlling systems of mobile robots. The results of this study can be used to address a navigation task, as well as path planning, capturing and manipulating scene objects, and semantic scene mapping.

An Algorithm for Evaluating the Results of Statistical Analysis of Biomedical Data under the Conditions of the Effect of Multiple Comparisons

The paper proposes an algorithm for assessing the significance of differences in the problem of managing research design under the conditions of the effect of multiple comparisons. Using the example of the problem of comparing data coming from paired channels of an electroencephalograph, the application of the algorithm is described.

On the Issue of Designing a System for Continuous Control of Road Materials Compaction for Pavers

The results of the research on the creation of an automatic compaction control system (ACCS) for pavers in real time are considered. The research is based on the methods of artificial neural networks (ANN). In this paper, an ANN model is obtained, with the help of which it is possible to determine the compaction coefficient (CC) of an asphalt mixture. The input variables of the ACCS are the velocity of movement of the paver, the frequency of impacts of the tamper, the force in the pusher of the tamper, the type of mixture, the thickness of the layer. The results of a computational experiment on the calculation of Cc in real time are presented. The ANN is able to explain more than 98 % of the measured data.

Program Complex for Automated Calculation of the Geometry of Impact Units of Machines

Improving the designs of impact mining machines in order to increase the productivity of drilling operations requires calculations of the geometric parameters of impact units. The greatest effect when the impact is applied to the rock is given by an impact pulse corresponding to the resistance forces of the object being destroyed. In turn, the shape and parameters of the impact pulse are determined by the geometry of the colliding bodies. Analytical methods for analyzing dynamic processes in impact systems involve the use of a very complex mathematical apparatus, which does not allow us to quickly solve the problems of engineering design of machines and mechanisms. The authors of this article have developed a numerical method for calculating and analyzing impact pulses generated in the machine system by bodies of any geometric complexity. The reliability of the theoretical approaches is confirmed by the results of a physical experiment. The developed software allows you to quickly and accurately solve the problem of finding and justifying rational geometric parameters of impact nodes of machines.

Parallel Adaptive Interpolation Algorithm based on Sparse Grids for Modeling Dynamic Systems with Interval Parameters

The paper presents a parallel algorithm for adaptive interpolation based on sparse grids for modeling dynamic systems with interval parameters. The idea of the algorithm is to construct a piecewise polynomial function that interpolates the dependence of the solution to the problem on the point values of the interval parameters. In the classical version of the algorithm, polynomial interpolation on complete grids is used, and with a large number of uncertainties, the algorithm becomes difficult to apply due to the exponential growth of computational costs. The use of sparse grids can significantly reduce the computational costs, but nevertheless the complexity of the algorithm in the general case remains exponential with respect to the number of interval parameters. In this regard, the issue of accelerating the algorithm is relevant. The algorithm can be divided into several sets of independent subtasks: updating the values corresponding to the grid nodes; calculation of weighting factors; interpolation of values at new nodes. The last two sets imply parallelization of recursion, so here the techniques for traversing the width of the call graph are mainly used. The parallel implementation of the algorithm was tested on two ODE systems containing two and six interval parameters, respectively, using a different number of computing cores. The results obtained demonstrate the effectiveness of the approaches used.

Integrity as an Aspect of Information Security: an Overview of Modern Apрroaches

There are three main aspects of the information security: • confidentiality; • availability; • integrity. Nowadays ensuring the confidentiality has ceased to be the dominant requirement, but privacy as an element of confidentiality (or as an independent aspect) attracts, perhaps, the greatest attention both at the legislative (personal data protection) and at the technical (depersonalization of big data) levels. The Internet of Things has defined a new level of accessibility requirements. Network access has come to the fore, without it a (too) smart kettle will not boil water. (Attempts to block some Internet resources led to similar everyday problems.) The number of entities whose integrity needs to be ensured has grown. These are data flows, hardware configurations, logistics chains, and much more. Integrity began to be understood not only as security against unauthorized modification, but also as the quality of data, their completeness and validity. This was the reason for writing this article. In our opinion, there is no systematic presentation of modern approaches to ensuring integrity in the literature and therefore it would be appropriate. It makes no sense to rank the aspects of information security according to their importance, but we would like to emphasize the role of integrity. Integrity turns out to be the most important aspect of information security in those cases when information is a "guide to action" and serves to make decisions. The prescription of medicines, prescribed medical procedures, the set and characteristics of components, the course of the technological process — all these are examples of information, the violation of the integrity of which can literally be fatal. It is also unpleasant to distort official information, whether it is the text of the law or the page of the website of a government organization. Compromising the integrity of the electronic voting process casts doubt on the legitimacy of the authorities. Unauthorized influence on financial flows leads to material losses. Distortion of navigation data can lead to accidents. The article is an overview of modern approaches to ensuring integrity as an aspect of information security. An attempt is made to analyze the relevant models, policies and security mechanisms, as well as their application in different subject areas.

Towards an Adaptive Educational Course on the Mathematical Foundations of Machine Learning

Today the development of information technology is closely related to the creation and application of machine learning and data analysis methods. In this regard, the need for training specialists in this area is growing. Very often, the study of machine learning methods is combined with the study of a certain programming language and the tools of its specialized library. This approach is undoubtedly justified, because it provides the possibility of accelerated application of the knowledge gained in practice. At the same time, it should be noted that with this approach, it is rather not machine learning methods that are studied, but a certain set of methodological techniques for using the tools of the specialized library. The presented work is devoted to the experience of creating an adaptive educational course on the mathematical foundations of machine learning. This course is aimed at undergraduate and graduate students of mathematical specialties. It is divided into core and variable parts. The obligatory core part is built around the PAC learning model and the binary classification problem. Within the variable part, issues of the weak learning model and the boosting methods are considered. Also a methodology of changing the variable part of the course is discussed.