Mathematical modeling estimates of the reliability of rumors in mass media

2019 ◽  
Vol 5 (4) ◽  
pp. 181-199
Author(s):  
Alexander A. Chernyaev ◽  
Alexander G. Ivashko
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Mass Media ◽  
Russian Language ◽  
News Article ◽  
News Sources ◽  
Huge Data ◽  
The Neural Network ◽  
High Level ◽  
The Russian Language

One of the most important tasks of the contemporary society includes fighting the spreading false information. The unprecedented transition from the traditional media to the modern methods of receiving news has created many problems with verifying its authenticity. Contemporary journalists have to compete with a huge data stream of ordinary users, which is why the main quality factor is the time to publish a news article. As a result, an increasing number of traditional news sources report unclarified information due to the rush to be first. This paper considers a method for determining the presence of hearing in the mass media for the Russian language. This method aims to study the possibility of searching for rumors among users’ messages in social networks. Achieving this goal requires various methods of text analysis, including semantic and linguistic analysis, as well as the analysis of the distribution of records relative to time segments. During the research, the authors have analyzed different popular tools for obtaining data from social networks. In addition, they have manually compiled and marked a sample for training the neural network. As a tool for solving the problem, we used a neural network based on a multi-layer perceptron. The inputs receive a set of 15 metrics that evaluate all aspects of hearing, and as an output, the probability of hearing. The test was performed using various metrics that showed high results for the constructed neural network model. Cross-validation has shown that the model is able to withstand various checks at a high level.

Mediadiscourse of fashion: processes, phenomena, effects

2021 ◽  
Author(s):  
Violetta Gaputina
Keyword(s):  
Social Networks ◽  
Mass Media ◽  
Russian Language ◽  
Media Discourse ◽  
Fashion Industry ◽  
Main Attention ◽  
Language Discourse ◽  
Journalism Students ◽  
Modern Mass ◽  
The Russian Language

The monograph is devoted to the study of the Russian-language discourse of fashion, actualized in the space of modern mass media: in television broadcasts and glossy magazines, blogs and social networks. The main attention is paid to the processes of hybridization of fashion discourse and media discourse and their linguistic and speech manifestations, reflecting the intersection of fashion discourse with other types of discourse. The book is addressed to specialists in the field of media linguistics and journalism, students, teachers and researchers, employees of the fashion industry, as well as all those who are interested in fashion and style issues.

scholarly journals Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 52
Author(s):  
Richard Evan Sutanto ◽  
Sukho Lee
Keyword(s):  
Neural Network ◽  
Attack Detection ◽  
Detection Methods ◽  
Defense System ◽  
Image Prior ◽  
The Neural Network ◽  
Adversarial Examples ◽  
Deep Image ◽  
Adversarial Attack ◽  
High Level

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.

scholarly journals Prediction of Mining Conditions in Geotechnically Complex Sites

Mining ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 279-296
Author(s):  
Marc Elmouttie ◽  
Jane Hodgkinson ◽  
Peter Dean
Keyword(s):  
Neural Network ◽  
Historical Data ◽  
Synthetic Data ◽  
Mining Operations ◽  
Body Of Knowledge ◽  
Future Production ◽  
The Neural Network ◽  
Mining Conditions ◽  
Parameter Interactions ◽  
High Level

Geotechnical complexity in mining often leads to geotechnical uncertainty which impacts both safety and productivity. However, as mining progresses, particularly for strip mining operations, a body of knowledge is acquired which reduces this uncertainty and can potentially be used by mining engineers to improve the prediction of future mining conditions. In this paper, we describe a new method to support this approach based on modelling and neural networks. A high-level causal model of the mining operations based on historical data for a number of parameters was constructed which accounted for parameter interactions, including hydrogeological conditions, weather, and prior operations. An artificial neural network was then trained on this historical data, including production data. The network can then be used to predict future production based on presently observed mining conditions as mining proceeds and compared with the model predictions. Agreement with the predictions indicates confidence that the neural network predictions are properly supported by the newly available data. The efficacy of this approach is demonstrated using semi-synthetic data based on an actual mine.

scholarly journals APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN THE DENTAL CARIES FORECAST

2020 ◽  
Vol 73 (7) ◽  
pp. 1499-1504
Author(s):  
Oleksandr A. Udod ◽  
Hanna S. Voronina ◽  
Olena Yu. Ivchenkova
Keyword(s):  
Neural Network ◽  
Dental Caries ◽  
Old Patients ◽  
Software Application ◽  
The Neural Network ◽  
Software Product ◽  
High Level ◽  
Network Technologies ◽  
Python Programming ◽  
And Training

The aim: of the work was to develop and apply in the clinical trial a software product for the dental caries prediction based on neural network programming. Materials and methods: Dental examination of 73 persons aged 6-7, 12-15 and 35-44 years was carried out. The data obtained during the survey were used as input for the construction and training of the neural network. The output index was determined by the increase in the intensity of caries, taking into account the number of cavities. To build a neural network, a high-level Python programming language with the NumPay extension was used. Results: The intensity of carious dental lesions was the highest in 35-44 years old patients – 6.69 ± 0.38, in 6-7 years old children and 12-15 years old children it was 3.85 ± 0.27 and 2.15 ± 0.24, respectively (p <0.05). After constructing and training the neural network, 61 true and 12 false predictions were obtained based on these indices, the accuracy of predicting the occurrence of caries was 83.56%. Based on these results, a graphical user interface for the “CariesPro” software application was created. Conclusions: The resulting neural network and the software product based on it permit to predict the development of dental caries in persons of all ages with a probability of 83.56%.

The Russian Language In the Social Networks

2016 ◽  
Vol 5 (2) ◽  
pp. 35-40
Author(s):  
Селеменева ◽  
O. Selemeneva
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
The State ◽  
Russian Language ◽  
Social Needs ◽  
The Social ◽  
The Russian Language ◽  
Written Speech

The article examines the problem of the functioning of the Russian language in the texts of the online social networks as means of realization of the social needs and the organization of the communication between people. The author supposes that such texts are a mirror of the state of the Russian society and the Russian language. The dominance of the factual tone of Internet discourse, orientation on the dialogue, emotion and aggression of the communication leads to the changes in the representation of the verbalized and non-verbalized knowledge, to the increase of the ways of language compression, to the use of the constructions of expressive syntax, to the fall of the culture of written speech. Slang and colloquial vocabulary with terminology at the same time, segmented structures, emoticons, the reduplication of the punctuation marks, the abbreviation, the allocation of front are used in the texts of social networks.

Dynamics of Communication in the Sphere of Speech Etiquette

2020 ◽  
Vol 9 (1) ◽  
pp. 21-26
Author(s):  
N. Basko
Keyword(s):  
Mass Media ◽  
Computer Technology ◽  
English Language ◽  
Russian Language ◽  
Present Stage ◽  
Communicative Act ◽  
The Impact ◽  
The Russian Language ◽  
Modern Russian

The article discusses the changes in communication that have occurred in the Russian speech etiquette, on the example of etiquette forms of greeting. Speech etiquette is the most important element of a communicative act. Compliance with the rules of speech etiquette largely ensures success in solving communicative problems. Based on the analysis of lexicographical sources and materials of modern Russian mass media, a shift in the use of greeting forms is noted. It is expressed in the transfer of old forms of greeting to a passive stock and the emergence and active use of new forms of greeting The author concludes that the dynamics of changes in speech forms of greeting reflects the general trends in the development of the Russian language at the present stage, such as a) the active neologization; b) the influence of the English language; c) the impact of computer technology on the language.

Fitting potential energy surfaces to sum-of-products form with neural networks using exponential neurons

2017 ◽  
Vol 16 (05) ◽  
pp. 1730001 ◽  
Author(s):  
Alex Brown ◽  
E. Pradhan
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
The Neural Network ◽  
Sum Of Products ◽  
High Level ◽  
Energy Surfaces ◽  
Dynamics Problems

In this paper, the use of the neural network (NN) method with exponential neurons for directly fitting ab initio data to generate potential energy surfaces (PESs) in sum-of-product form will be discussed. The utility of the approach will be highlighted using fits of CS2, HFCO, and HONO ground state PESs based upon high-level ab initio data. Using a generic interface between the neural network PES fitting, which is performed in MATLAB, and the Heidelberg multi-configuration time-dependent Hartree (MCTDH) software package, the PESs have been tested via comparison of vibrational energies to experimental measurements. The review demonstrates the potential of the PES fitting method, combined with MCTDH, to tackle high-dimensional quantum dynamics problems.

A neural network potential energy surface for the NaH2 system and dynamics studies on the H(2S) + NaH(X1Σ+) → Na(2S) + H2(X1Σg+) reaction

2017 ◽  
Vol 19 (30) ◽  
pp. 19873-19880 ◽  
Author(s):  
Shufen Wang ◽  
Jiuchuang Yuan ◽  
Huixing Li ◽  
Maodu Chen
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
High Level

A new potential energy surface of the NaH2 system is obtained using the neural network method based on high-level energies.

scholarly journals Feature-Based Interpretation of the Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2687
Author(s):  
Eun-Hun Lee ◽  
Hyeoncheol Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Lower Layer ◽  
Network Models ◽  
Neural Network Models ◽  
Network Pruning ◽  
The Neural Network ◽  
Single Instance ◽  
High Level

The significant advantage of deep neural networks is that the upper layer can capture the high-level features of data based on the information acquired from the lower layer by stacking layers deeply. Since it is challenging to interpret what knowledge the neural network has learned, various studies for explaining neural networks have emerged to overcome this problem. However, these studies generate the local explanation of a single instance rather than providing a generalized global interpretation of the neural network model itself. To overcome such drawbacks of the previous approaches, we propose the global interpretation method for the deep neural network through features of the model. We first analyzed the relationship between the input and hidden layers to represent the high-level features of the model, then interpreted the decision-making process of neural networks through high-level features. In addition, we applied network pruning techniques to make concise explanations and analyzed the effect of layer complexity on interpretability. We present experiments on the proposed approach using three different datasets and show that our approach could generate global explanations on deep neural network models with high accuracy and fidelity.

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