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Abstract: A new architecture for the synthesis of fault-tolerant digital devices, which is easier to implement as compared to the well-known architecture based on triple modular redundancy (TMR), is proposed. The architecture is implemented based on the Boolean complement principle, which implies the use of a special control block for evaluating complement functions, rather than by introducing exact copies of an original circuit. In practice, its complexity can be significantly lower than the complexity of the original circuit. This makes it possible to synthesize fault-tolerant devices with simpler designs as compared to TMR-based devices. The proposed architecture consists of three blocks: the original circuit, the signal error detection circuit, and the signal correction circuit. The synthesis of a fault-tolerant digital device is aimed at generating the structure of the signal error detection circuit, which implements the idea of duplication of complements. The advantages and disadvantages of the proposed fault-tolerant architecture are discussed. The results of experiments on some combinational benchmarks, which demonstrate the effectiveness of the proposed approach, are presented. PubDate: 2022-02-01
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Abstract: There is a design of an expressway lighting and early warning system based on MCU, and the corresponding control method is proposed in this paper. The system and its control method can realize the lighting and direction guidance of the vehicles on the expressway under different situations. The different colors of lights are used to remind the vehicle of the different driving situation. The emergent buttons and GSM module can realize help-calling for people in distress in the expressway and can also be used for road maintenance personnel to locate the worker and check on work attendance. The system and its control method can also realize the vehicle counting function and the calculation of magnitude of traffic flow. PubDate: 2022-02-01
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Abstract: Inter-organizational cloud computing model is beneficial for academic institutions. We propose a collaborative inter-institutional cloud learning, where cloud entities can learn the same model cooperatively. The inter-institutional cloud model can reduce network bandwidth costs and ensure privacy. We recommend a trainer to student’s/customer strategy. The key role is to combine a regularization term with the objective function to adjust the gradient of the inter-institutional students/customers under different data. Then, based on the training strategy, a robust federated optimization technique based on joint identification verification is proposed to reduce the number of communication rounds. The main aims of this paper are, first, it can provide a technical platform for cloud services, then institutional resources utilization and opportunistic resources have been evaluated for their overall usage of cloud services and goals. Secondly, our time allocation for cloud computing management workloads is an active area of investigation metrics for the current scenario and control strategy. Experimental results analyze through the different case studies and use the scalability technology compared with the academia and robust scalability techniques. PubDate: 2022-02-01
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Abstract: In today’s era of information expansion, there exist a lot of real-time news every day, and there will be a time difference between potential real-time hotspots and hot news. It is time-consuming for IT operators to read all the real-time news and find the hot spots by human effort. Therefore, the prediction of hot news is particularly important for we-media operation. Therefore, this paper attempts to use natural language processing technology to predict hot news and to assist operators to create articles about the hot news. For hot news prediction, this paper proposes a method of data preprocessing and uses several models based on convolutional neural networks (CNNs), text convolutional neural networks (TextCNNs), long-short term memory (LSTM), bi-directional long short-term memory (BiLSTM) to train. We also build a new model—CNN Distinct and BiLSTM Extract, which can be called CDBE for short—to obtain better performance. We evaluate several training models and analyze them by multiple evaluation indexes. In addition, we apply the method proposed in this paper to actual operation work, and the result shows that such can greatly reduce operators’ pressure, save the working time for creating articles about hot news, and greatly improve their work efficiency. PubDate: 2022-02-01
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Abstract: Wireless sensor networks (WSNs) are composed of tiny sensors nodes with limited resources, and communicating together to monitor the environment. Sensor nodes are usually powered by battery. Consequently, the energy efficiency is critical for the lifetime of the Wireless sensors network. Routing protocols are the most important issue for WSNs. LEACH (low energy adaptive clustering hierarchy) is one of the first hierarchical routing algorithms for WSNs. LEACH-C (LEACH-centralized) is a variant of LEACH where the cluster heads are selected by the Sink. At the beginning of each round in LEACH-C, the sensor nodes must send information about their current energy to the Sink, which exhausts the sensor node battery. This paper proposes an improved LEACH-C algorithm in which, the Sink uses a consumption mode for each sensor node (CMSN) to estimate the amount of energy needed for the next rounds; so, sensor nodes doesn’t have to send the value of the current energy to the Sink at the beginning of each round. The simulation results and analysis show that our proposed protocol achieve much better performances in terms of energy dissipation and network lifetime compared to LEACH-C. PubDate: 2022-02-01
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Abstract: — This work studies Orbot, an anonymous overlay network used to browse the Internet. Its ease of use has attracted all kinds of people, including ordinary Internet users who want to avoid being profiled to bypass censorship, government intelligence agencies that need to do operations on the Internet without being detected and companies who do not want to reveal information to their competitors. This article aims to study, analyze, and mostly identify the Orbot traffic, since much of it is used for illegal purposes. A method of identification of the anonymous network is established by examining the traffic to identify clues. The method used to detect the use of the Orbot application in the network is based on the creation of the rules with Snort IDS from the analysis of the packets in Wireshark analyzer. The encryption aspect of the flow of this anonymous network brings us to a deep packet inspection (DPI). A set of Snort rules were developed as a proof of concept for the proposed Orbot detection approach. Our traffic detection methodology has demonstrated that it can detect Orbot connections in real time. PubDate: 2022-02-01
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Abstract: This study is aimed to analyze the characteristics of different kinds of time-of-flight diffraction (TOFD) images for welding seam defects. Combined with the image recognition technology of artificial intelligence, a deep learning neural network program based on TensorFlow was developed and applied to the training and recognition of welding seam defects in ultrasonic TOFD images. The results showed that, after training, the program could identify typical welding seam defects such as stoma, crackle, slag inclusion, lack of fusion, and incomplete penetration in TOFD images of the welding seam, and the recognition confidence was more than 0.8. This proved that the program developed in this study provided an effective reference for determining typical welding seam defects in TOFD images of the welding seam. PubDate: 2022-02-01
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Abstract: The aim of this paper is to propose the convolution neural network—VGG16 structure in Keras to classify pavement defects. In this paper, we present a method to build an automated system to classify different types of defects such as block cracks, longitudinal cracks and potholes. A region of interest is found and features are extracted using image processing techniques and machine learning methods. This system includes the following steps. The first step is to detect the defect location (ROI), then the defect is described by its features. Finally, each defect is classified based on these different features. The system ensures stable operation in the presence of limited lighting conditions, shadowing, and complex shaped defects. PubDate: 2022-02-01
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Abstract: FCM algorithm is one of the well-known techniques for image segmentation; it is based on an imprecise decision by using the membership function. However, FCM algorithm fails to proceed well enough in the presence of imaging artifacts due to its performance without any consideration of spatial information. In this paper, we propose two crucial modifications to the conventional FCM algorithm to tackle its sensitivity against noise. Firstly, the proposed algorithm provides full consideration of the spatial constraint, wherein the influence of neighboring pixels is defined according to two proposed terms, a fuzzy similarity measure as well as the level of noise. Secondly, we adopt a strategy to select the optimal pixel between the central pixel and its neighboring pixels that can better influence the segmentation performance in terms of compactness and separation information. The proposed algorithm is compared qualitatively and quantitatively with five existing clustering methods in terms of cluster validity functions, segmentation accuracy, tissue segmentation accuracy, and computational time. PubDate: 2022-02-01
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Abstract: A steganographic system based on linear mixing of two signals and nonlinear communication between container components, which is invariant to masking signal, is considered, and the key coefficient, which provides effective protection of hidden signal from unauthorized recovery, is determined. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080435
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Abstract: Considering the openness of industrial Internet-of-Things systems as compared with conventional SCADA-like systems, the provision of sustainable and continuous production becomes a more relevant and complex task. This article considers the application of the security-through-obscurity principle as the original method of maintaining production process continuity. An attempt to derive a formula for evaluating the protection level when using this principle is described. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080083
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Abstract: This article defines the possibility of using artificial neural networks for evaluating the probability of information safety threat occurrences and the development of a computer program. The result of analyzing the threat occurrence probability has shown that artificial neural networks can be used to evaluate the probability of information security threat occurrence. An application for evaluating the threat occurrence probability is developed. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080046
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Abstract: — Multidimensional data storage systems operating under ravage conditions and designed for storing big volumes of information are considered. An integrity control model based on Pascal’s cryptographic pyramid is presented for multidimensional data arrays. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080368
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Abstract: A system of authentication/authorization of users with administrative privileges was proposed. A mechanism for enhanced authentication and delegation of authority is proposed, which allows excluding the superuser by creating separate roles: network administrator, security administrator, virtual infrastructure administrator, APCS (automated process control system) administrator. The user authentication/authorization system is based on the use of the characteristics of the fourth formant and the frequency of the leading vowel formant. PubDate: 2021-12-01 DOI: 10.3103/S014641162108006X
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Abstract: In this paper, the principles of creating an algorithm for control and management of a remotely piloted sea vessel are described, along with its structure. The modes of a piloted tugboat motion under the influence of the port-specific interference are considered in detail. The characteristics and methods for calculating the interference effect to ensure the information security of communication channels of an unmanned vessel are presented. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080447
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Abstract: A crucial task of organizing environmental monitoring of a seaport was considered. The classification of information sources in the environmental monitoring system of the port water area was given. It was shown that it is advisable to use the vessel traffic control radars available in the port, which is justified by economic factors, the possibility of full coverage of the port water area with a radio signal, and the absence of interference sources for radar stations on the port territory. The energy characteristics when sounding the sea surface by ground radars of the vessel traffic control system were considered. The ratios of assessing the quantitative characteristics of the use of radars of the vessel traffic control system for detecting inhomogeneities of sea waves caused by oil and other pollution of the water surface were obtained. The graphical dependences of the signal-to-noise ratio were presented, demonstrating the possibility of using radars of the vessel traffic control system in the port waters for environmental monitoring of the sea surface. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080204
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Abstract: This article considers tools for controlling software installed on personal computers of automated system users. The flaws of these software solutions are grounded, and an approach to identifying executable files with the help of a machine learning algorithm is developed and presented. This algorithm consists in the gradient decision tree boosting on the basis of such libraries as XGBoost, LightGBM, CatBoost. The identification of programs with the help of XGBoost and LightGBM is executed. The experimental results are compared with the results of earlier studies conducted by other authors. The findings show that the developed method allows for identifying violations in the adopted security policy during information processing in automated systems. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080459
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Abstract: An approach to recognition of malicious behavior based on analysis of the Security.evtx security log of Windows operating system upon investigation into a security incident is given. The use of an autoregression model is experimentally tested (Change Finder algorithm), from which the malicious activity of the users of the domain in the corporate network is revealed. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080290
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Abstract: Some aspects of constructing the model of an information system suitable for further application in the problem of the automatization of penetration testing with the use of reinforcement machine learning methods are considered. The principal requirements to a similar model are formulated, and a prototype architecture of a similar system is proposed. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080216
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Abstract: In this paper, we propose a model for predicting the dynamics of a generalized efficiency index for corporate computer networks operating under conditions of malicious cyber activity. The model represents the dynamics of the index as a function of the operational efficiency of a corporate network at each instant on a certain time interval. In this case, the level of operational efficiency of the network depends on the operational efficiency of its components and is described by a system of differential equations that take into account both malicious activity and the process of eliminating its effects. Under some simplifying conditions, we find analytical solutions of these equations, which significantly facilitates the prediction of the dynamics of the generalized efficiency index. PubDate: 2021-12-01 DOI: 10.3103/S0146411621080332
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