scholarly journals Subjectness of Digital Communication in the Context of the Technological Evolution of the Contemporary Society: Threats, Challenges, and Risks

2021 ◽  
pp. 437-456
Author(s):  
Sergey Volodenkov ◽  
Sergey Fedorchenko
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Political Communication ◽  
Digital Communication ◽  
Digital Communications ◽  
Political Communications ◽  
Wide Range ◽  
Information And Communication ◽  
Communication Practices ◽  
Self Learning

The purpose of this article is to identify the risks, threats, and challenges associated with possible social changes in the processes of digitalization of society and transformations of traditional communication practices, which is associated with the emergence of new digital subjects of mass public communication that form the pseudo structure of digital interaction of people. The primary tasks of the work were to identify the potential of artificial intelligence technologies and neural networks in the field of social and political communications, as well as to analyze the features of “smart” communications in terms of their subjectness. As a methodological optics, the work used the method of discourse analysis of scientific research devoted to the implementation and application of artificial intelligence technologies and self-learning neural networks in the processes of social and political digitalization, as well as the method of critical analysis of current communication practices in the socio-political sphere. At the same time, when analyzing the current digitalization practices, the case study method was used. The authors substantiate the thesis that introducing technological solutions based on artificial intelligence algorithms and self-learning neural networks into contemporary processes of socio-political communication creates the potential for a wide range of challenges, threats, and risks, the key of which is the problem of identifying the actual subjects of digital communication acts. The article also discusses the problem of increasing the manipulative potential of “smart” communications, for which the authors used the concepts of cyber simulacrum and information capsule developed by them. The paper shows that artificial intelligence and self-learning neural network algorithms, being increasingly widely introduced into the current practice of contemporary digital communications, form a high potential for information and communication impact on the mass consciousness from technological solutions that no longer require control by operators – humans. As a result, conditions arise to form a hybrid socio-technical reality – a communication reality of a new type with mixed subjectness. The paper also concludes that in the current practices of social interactions in the digital space, a person faces a new phenomenon – interfaceization, within which self-communication stimulates the universalization and standardization of digital behavior, creating, disseminating, strengthening, and imposing special digital rituals. In the article, the authors suggest that digital rituals blur the line between the activity of digital avatars based on artificial intelligence and the activity of actual people, resulting in the potential for a person to lose his own subjectness in the digital communications space.

scholarly journals Subjectness of digital communication in the context of technological evolution of the Internet: features and transformation scenarios

2021 ◽  
Author(s):  
S.V. Volodenkov ◽  
S.N. Fedorchenko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Current Practice ◽  
Digital Communication ◽  
Social Practices ◽  
Network Algorithms ◽  
Political Sphere ◽  
Communication Processes ◽  
Self Learning

The work aimed to study the peculiarities of the subjectness of the phenomenon of digital communication in the context of intensive digitalization of key spheres of life of modern society, as well as to identify the prospects and threats of introducing self-learning neural network algorithms and artificial intelligence technologies into communication processes unfolding in the social and political sphere. One of the study's key objectives was to identify scenarios of possible social changes in the context of society digitalization and the traditional social practices transformation in terms of the emergence of new digital subjects of mass public communication that form the pseudo structure of digital interaction between people. As a methodological optics, the work used the method of discourse analysis of scientific research devoted to the implementation and application of artificial intelligence technologies and self-learning neural networks in the processes of socio-political digitalization, as well as the method of critical analysis of current communication practice in the socio-political sphere. At the same time, when analyzing the current practice of digitalization in foreign countries, the case study method was used. In turn, to determine the scenarios for the transformation of traditional social space and social practices, the method of scenario techniques and scenario forecasting was applied. As a research result, it was concluded that the introduction of technological solutions based on artificial intelligence algorithms and self-learning neural networks into contemporary socio-political communication processes creates the potential for the problem of identifying the subjects of communicative acts in the socio-political sphere of the contemporary society life. Based on the results of the study, it is shown that artificial intelligence and self-learning neural network algorithms are increasingly being implemented in the current practice of contemporary digital communications, forming a high potential for information and communication impact on the mass consciousness of technological solutions that no longer require self-control from human operators. The work also concludes that in the current practice of social interactions in the digital space, a person faces a new phenomenon – interfaceization, within which self-communication stimulates the universalization and standardization of digital behavior, creating, disseminating, strengthening, and imposing special digital rituals. The article proves that digital rituals blur the line between digital avatars' activity based on artificial intelligence and the activity of real people, resulting in the potential for a person to lose their own subjectness in the digital universe.

scholarly journals Development of E-government in Russia: Problems and Prospects

2019 ◽  
pp. 60-68
Author(s):  
T. N. Litvinova
Keyword(s):  
Information And Communication Technologies ◽  
Political Communication ◽  
Computer Literacy ◽  
Communication Technologies ◽  
Electronic Government ◽  
Development Index ◽  
Study Results ◽  
Wide Range ◽  
Information And Communication ◽  
Government Development

Introduction. The article gives an assessment of the e-government development in Russia from 2008 to 2018. E-government contributes to the development of the state’s information infrastructure, improves the effciency of public service delivery to the society and attracts the public to participate in the process of developing and adopting government decisions. The article presents a comparative analysis of the development of the electronic government of Russia with other countries. The key issues of improving e-government in Russia are identifed on the basis of the UN e-government development index. This indicator allows assessing whether the state is ready to provide electronic public services to citizens and what are its opportunities for using information and communication technologies in providing these services.Materials and methods. Electronic government has become the subject of a wide range of disciplines, including political communication and sociology. Currently, scientists are paying increasing attention to the intersection of technological factors, equipment and culture in the adoption and use of information and communication technologies (ICT), e-government research has begun to demonstrate some diversifcation. Russian scientists mostly focus on the statistic data of implementation of egovernment and consequences for governance and society. This investigation is based on following methods: 1) content-analysis of offcial documents of the Russian Federation concerning e-government; 2) declarations and interviews of offcial authorities; 3) monitoring of mass media; 3) international and national statistics data analysis.Study results. Russia has relatively good indicators of e-government development in the world (according to UN e-Government Development Index), and the introduction of e-government is quite fast. But, in comparison with the leading countries, Russia still lags far behind in many respects because of: huge territory; low level of distribution of electronic services; low activity of mobile communication; weak dynamics of the increase in the number of Internet users; lack of the necessary law regulatory framework; low computer literacy of many government offcials.

scholarly journals Artificial Neural Networks in Agriculture

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Sebastian Kujawa ◽  
Gniewko Niedbała
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transport Processes ◽  
High Quality Research ◽  
Wide Range ◽  
Brain Structure And Function ◽  
Artificial Neural ◽  
And Function

Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.

scholarly journals Regarding Solid Oxide Fuel Cells Simulation through Artificial Intelligence: A Neural Networks Application

2018 ◽  
Vol 9 (1) ◽  
pp. 51 ◽  
Author(s):  
Arianna Baldinelli ◽  
Linda Barelli ◽  
Gianni Bidini ◽  
Fabio Bonucci ◽  
Feride Iskenderoğlu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuel Cells ◽  
Solid Oxide Fuel Cells ◽  
Current Density ◽  
Adaptive Learning ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Data Set ◽  
Wide Range

Because of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8YSZ/LSCF anode supported planar button cells. The best simulator obtained is a 3-hidden layer ANN (25/22/18 neurons per layer, hyperbolic tangent sigmoid as transfer function, obtained with a gradient descent with adaptive learning rate backpropagation). This shows high accuracy (RMS = 0.67% in the testing phase) and successful application in the forecast of SOFC polarization behaviour in two additional experiments (RMS in the order of 3% is scored, yet it is reduced to about 2% if only the typical operating current density range of real application is considered, from 300 to 500 mA·cm−2). Therefore, the neural tool is suitable for system simulation codes/software whether SOFC operating parameters agree with the input ranges (anode feeding composition 0–48%vol H2, 0–38%vol CO, 0–45%vol CH4, 9–32%vol CO2, 0–54%vol N2, specific equivalent hydrogen flow-rate per unit cell active area 10.8–23.6 mL·min−1·cm−2, current density 0–1300 mA·cm−2 and temperature 700–800 °C).

scholarly journals Optimization of Extrusion-Based 3D Printing Process Using Neural Networks for Sustainable Development

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2737
Author(s):  
Izabela Rojek ◽  
Dariusz Mikołajewski ◽  
Marek Macko ◽  
Zbigniew Szczepański ◽  
Ewa Dostatni
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Risk Assessment ◽  
Sustainable Development ◽  
3D Printing ◽  
Computational Intelligence ◽  
Learning Program ◽  
Printing Process ◽  
Printing Processes ◽  
Self Learning

Technological and material issues in 3D printing technologies should take into account sustainable development, use of materials, energy, emitted particles, and waste. The aim of this paper is to investigate whether the sustainability of 3D printing processes can be supported by computational intelligence (CI) and artificial intelligence (AI) based solutions. We present a new AI-based software to evaluate the amount of pollution generated by 3D printing systems. We input the values: printing technology, material, print weight, etc., and the expected results (risk assessment) and determine if and what precautions should be taken. The study uses a self-learning program that will improve as more data are entered. This program does not replace but complements previously used 3D printing metrics and software.

scholarly journals Analog environment vs. digital communications

2020 ◽  
Vol 1 ◽  
pp. 11-34
Author(s):  
Rusi Marinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
New Technologies ◽  
Digital Communications ◽  
Radical Change ◽  
Digital Tools ◽  
Human Environment ◽  
Interaction Models ◽  
Traditional Approaches

In this article, I discuss problems associated with new technologies, digital communications and the future of analog interaction models. I also analyze the development possibilities of artificial intelligence and neural networks based on analog computhttping systems. The transformation today, involves a radical change in existing models and the rediscovery of the benefits of some traditional approaches, which in another context can be much more effective than existing information and digital tools. In this case, it is the analog approaches of quantum computing in combination with new technologies that lead to better results, development of society and the creation of a more human environment.

scholarly journals THE USE OF ARTIFICIAL INTELLIGENCE METHODS FOR APPROXIMATION OF THE MECHANICAL BEHAVIOR OF RUBBER-LIKE MATERIALS

2021 ◽  
pp. 95-99
Author(s):  
Oleksii Vodka ◽  
Serhii Pohrebniak
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Hysteresis Loops ◽  
Test Sample ◽  
Training Sample ◽  
The Neural Network ◽  
Internal Structures ◽  
Wide Range ◽  
Software Product ◽  
Direct Distribution

In the XXI century, neural networks are widely used in various fields, including computer simulation and mechanics. This popularity is due to the factthat they give high precision, work fast and have a very wide range of settings. The purpose of creating a software product using elements of artificialintelligence, for interpolation and approximation of experimental data. The software should work correctly, and yield results with minimal error. Thedisadvantage of using mathematical approaches to calculating and predicting hysteresis loops is that they describe unloading rather poorly, thus, weobtain incorrect data for calculating the stress-strain state of a structure. The solution tool use of elements of artificial intelligence, but rather neuralnetworks of direct distribution. The neural network of direct distribution has been built and trained in this work. It has been trained with a teacher (ateacher using the method of reverse error propagation) based on a learning sample of a pre-experiment. Several networks of different structures werebuilt for testing, which received the same dataset that was not used during the training, but was known from the experiment, thus finding a networkerror in the amount of allocated energy and in the mean square deviation. The article describes in detail the mathematical interpretation of neuralnetworks, the method for training them, the previously conducted experiment, structure of network that was used and its topology, the training method,preparation of the training sample, and the test sample. As a result of the robots carried out, the software was tested in which an artificial neuralnetwork was used, several types of neural networks with different input data and internal structures were built and tested, the error of their work wasdetermined, the positive and negative sides of the networks that were used were formed.

scholarly journals Adversarial Examples in Physical World

2021 ◽  
Author(s):  
Jiakai Wang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Potential Risk ◽  
Real World ◽  
Deep Neural Networks ◽  
Physical World ◽  
The Real ◽  
Wide Range ◽  
Adversarial Examples ◽  
Ai Safety

Although deep neural networks (DNNs) have already made fairly high achievements and a very wide range of impact, their vulnerability attracts lots of interest of researchers towards related studies about artificial intelligence (AI) safety and robustness this year. A series of works reveals that the current DNNs are always misled by elaborately designed adversarial examples. And unfortunately, this peculiarity also affects real-world AI applications and places them at potential risk. we are more interested in physical attacks due to their implementability in the real world. The study of physical attacks can effectively promote the application of AI techniques, which is of great significance to the security development of AI.

scholarly journals ONLINE MODIFICATION OF THE METHOD OF X-MEDIUM ON THE BASIS OF ANSAMBLY OF SELORGANIZED MAP T. KOHONEN

2017 ◽  
pp. 96-107
Author(s):  
Є.В. БОДЯНСЬКИЙ ◽  
А.О. ДЕЙНЕКО ◽  
П.Є. ЖЕРНОВА ◽  
В.О. РЄПІН
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Data Sets ◽  
Self Organizing Maps ◽  
Online Mode ◽  
Wide Range ◽  
Parallel Mode ◽  
Computational Simplicity ◽  
Self Learning

The modified X-means method for clustering in the case when observations are sequentially fed to processing the proposed. This approach’s based on the ensemble of the clustering neural networks, proposed ensemble contains the T. Kohonen’s self-organizing maps. Each of the clustering neural networks consist of different number of neurons, where number of clusters is connected with the quality of there neurons. All ensemble members process information that siquentionally is fed to the system in the parallel mode. The effectiveness of clustering process is determined using Caliński-Harabasz index. The self-learning algorithm uses similarity measure of special type that. The feature of proposed method is absent of the competition step, i.e. neuron-winner is not determined. A number of experiments has been held in order to investigate the proposed system’s properties. Experimental results have proven the fact that the system under consideration could be used to solve a wide range of Data Mining tasks when data sets are processed in an online mode. The proposed ensemble system provides computational simplicity, and data sets are pro-cessed faster due to the possibility of parallel tuning.

scholarly journals Cattle Detection Using Oblique UAV Images

2020 ◽  
Author(s):  
Jayme Barbedo ◽  
Luciano Koenigkan ◽  
Patrícia Santos
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Unmanned Aircraft ◽  
Classification Error ◽  
Deep Convolutional Neural Networks ◽  
Wide Range ◽  
Sensor Effect ◽  
Uav Images ◽  
Oblique Images

The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for the experiments, which covered aspects like ideal input dimensions, effect of the distance between animals and sensor, effect of classification error on the overall detection process, and impact of physical obstacles on the accuracy of the model. Experimental results indicate that oblique images can be successfully used under certain conditions, but some practical limitations need to be addressed in order to make this approach appealing.

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