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Industrial imaging or “machine vision” is currently a key technology in many industries as it can be used to optimize various processes. The purpose of this work is to create a software and hardware complex for measuring the overall and weight characteristics of cargo based on an intelligent system using neural network identification methods that allow one to overcome the technological limitations of similar complexes implemented on ultrasonic and infrared measuring sensors. The complex to be developed will measure cargo without restrictions on the volume and weight characteristics of cargo to be tariffed and sorted within the framework of the warehouse complexes. The system will include an intelligent computer program that determines the volume and weight characteristics of cargo using the machine vision technology and an experimental sample of the stand for measuring the volume and weight of cargo.

We analyzed the solutions to similar problems. We noted that the disadvantages of the studied methods are very high requirements for the location of the camera, as well as the need for manual operations when calculating the dimensions, which cannot be automated without significant modifications. In the course of the work, we investigated various methods of object recognition in images to carry out subject filtering by the presence of cargo and measure its overall dimensions. We obtained satisfactory results when using cameras that combine both an optical method of image capture and infrared sensors. As a result of the work, we developed a computer program allowing one to capture a continuous stream from Intel RealSense video cameras with subsequent extraction of a three-dimensional object from the designated area and to calculate the overall dimensions of the object. At this stage, we analyzed computer vision techniques; developed an algorithm to implement the task of automatic measurement of goods using special cameras and the software allowing one to obtain the overall dimensions of objects in automatic mode.

Upon completion of the work, this development can be used as a ready-made solution for transport companies, logistics centers, warehouses of large industrial and commercial enterprises.

A crucial component of distributed computing systems is network infrastructure. While networking forms the backbone of such systems, it is often the invisible partner to storage and computing resources. We propose to integrate Network Elements directly into distributed systems through the workload management layer. There are many reasons for this approach. As the complexity and demand for distributed systems grow, it is important to use existing infrastructure efficiently. For example, one could use network performance measurements in the decision making mechanisms of workload management systems. New advanced technologies allow one to programmatically define network configuration, for example SDN — Software Defined Networks. We will describe how these methods are being used within the PanDA workload management system of the ATLAS collaboration.

The work solves the problem of establishing the dependence of the potential for spatial selection of useful and interfering signals according to the signal-to-interference ratio criterion on the positioning error of user equipment during beamforming by their location at a base station, equipped with an antenna array. Configurable simulation parameters include planar antenna array with a different number of antenna elements, movement trajectory, as well as the accuracy of user equipment location estimation using root mean square error of coordinate estimates. The model implements three algorithms for controlling the shape of the antenna radiation pattern: 1) controlling the beam direction for one maximum and one zero; 2) controlling the shape and width of the main beam; 3) adaptive beamforming. The simulation results showed, that the first algorithm is most effective, when the number of antenna array elements is no more than 5 and the positioning error is no more than 7 m, and the second algorithm is appropriate to employ, when the number of antenna array elements is more than 15 and the positioning error is more than 5 m. Adaptive beamforming is implemented using a training signal and provides optimal spatial selection of useful and interfering signals without device location data, but is characterized by high complexity of hardware implementation. Scripts of the developed models are available for verification. The results obtained can be used in the development of scientifically based recommendations for beam control in ultra-dense millimeter-wave radio access networks of the fifth and subsequent generations.

The paper reviews the main results of the study of real social networks (networks of collaboration between scientists and actors, networks of citation of scientific publications, networks of friends and acquaintances, etc.) and modern online social networks (Twitter, Facebook etc.) from the complex networks theory standpoint. Based on original research by the authors, it reveals peculiarities of perception of certain complex networks.

Disassembly of stripped x86 binaries is an important yet non-trivial task. Disassembly is difficult to perform correctly without debug information, especially on x86 architecture, which has variablesized instructions interleaved with data. Moreover, the presence of indirect jumps in binary code adds another layer of complexity. Indirect jumps impede the ability of recursive traversal, a common disassembly technique, to successfully identify all instructions within the code. Consequently, disassembling such code becomes even more intricate and demanding, further highlighting the challenges faced in this field. Many tools, including commercial ones such as IDA Pro, struggle with accurate x86 disassembly. As such, there has been some interest in developing a better solution using machine learning (ML) techniques. ML can potentially capture underlying compiler-independent patterns inherent for the compiler-generated assembly. Researchers in this area have shown that it is possible for ML approaches to outperform the classical tools. They also can be less timeconsuming to develop compared to manual heuristics, shifting most of the burden onto collecting a big representative dataset of executables with debug information. Following this line of work, we propose an improvement of an existing RGCN-based architecture, which builds control and flow graph on superset disassembly. The enhancement comes from augmenting the graph with data flow information. In particular, in the embedding we add Jump Control Flow and Register Dependency edges, inspired by Probabilistic Disassembly. We also create an open-source x86 instruction identification dataset, based on a combination of ByteWeight dataset and a selection open-source Debian packages. Compared to IDA Pro, a state of the art commercial tool, our approach yields better accuracy, while maintaining great performance on our benchmarks. It also fares well against existing machine learning approaches such as DeepDi.

Independent components communicating with each other due to complex control make the work of complex distributed computer systems poorly scalable within the framework of the existing communication middleware. Two major problems of such systems' scaling can be defined: overloading of unequal nodes due to proportional redistribution of workload and difficulties in the realization of continuous communication between several nodes of the system. This paper is focused on the developed solution enabling visualization of the work of such a dynamical system.