Analysis of the Legal Basis to Mitigate Cyberbullying in Social Networks in Ecuador

Cyberbullying is a critical issue in society worldwide, however in Ecuador is not given necessary importance to mitigate this cybercrime. It has been proposed to develop an exhaustive analysis of the laws that are currently considered to sanction cyberbullying when denouncing this fact. The deductive method and exploratory research were employed to make the analysis of the information consulted from the various sources that are obtained on the network about the topic discussed. The investigation revealed how cyberbullying cases arise, from which it is obtained that only 0.07% have been reported, with this result it can be deduced that the number of reported cases is very low in relation to the total number of cellphones activated in Ecuador and people who may be victims of this cyber-crime. In addition, a “Criminal Process Diagram” was obtained that determines the sequence of how the judicial process work. The applied method resulted the following: Can be created a law project that battles each type of derived cyberbullying. It was concluded that several institutions in Ecuador work together with organizations to prevent cyberbullying, however, when this happens, the laws are not enough to punish the act.

Federated Learning in Big Data Application and Sharing

In recent years, electricity big data has extensive applications in the grid companies across the provinces. However, certain problems are encountered including, the inability to generate an ideal model using the isolated data possessed by each company, and the priority concerns for data privacy and safety during big data application and sharing. In this pursuit, the present research envisaged the application of federated learning to protect the local data, and to build a uniform model for different companies affiliated to the State Grid. Federated learning can serve as an essential means for realizing the grid-wide promotion of the achievements of big data applications, while ensuring the data safety.

Interval Observer Design for Metzlerian Takagi-Sugeno Systems

The generalized interval observer design conditions for continuous-time Metzlerian Takagi-Sugeno systems are presented in the paper. Attention is focused on the analysis and design guaranteeing the asymptotic convergence of the interval observer error and positivity of interval observer state. The relationship between the nonnegativity of the observer gains and the corresponding positive observer state attractiveness is also shown. The method presented extends and generalizes the results that recently appeared in the literature.

Distance Metrics of D Numbers

Belief function has always played an indispensable role in modeling cognitive uncertainty. As an inherited version, the theory of D numbers has been proposed and developed in a more efficient and robust way. Within the framework of D number theory, two more generalized properties are extended: (1) the elements in the frame of discernment (FOD) of D numbers do not required to be mutually exclusive strictly; (2) the completeness constraint is released. The investigation shows that the distance function is very significant in measuring the difference between two D numbers, especially in information fusion and decision. Modeling methods of uncertainty that incorporate D numbers have become increasingly popular, however, very few approaches have tackled the challenges of distance metrics. In this study, the distance measure of two D numbers is presented in cases, including complete information, incomplete information, and non-exclusive elements

Fault Diagnosis of Coal Mining Machinery Based on State Parameters

The reliable operation of coal mining machinery acts as an important guarantee for safe productions in underground coal mines. The status monitoring and fault diagnosis of traditional coal mining machinery mainly rely on threshold judgments. However, a single judgment condition and a long fault propagation chain can be found in the method of threshold judgments, which make it difficult to accurately seek the fault type. By using the data analysis of state parameters for coal mining machinery, fault parameters and propagation paths can be analyzed effectively. This paper takes the cutting unit of a certain type of bolter miners as an example, a static and dynamic numerical analysis method of the cutting unit of bolter miners are established by virtue of FTA-Petri net models and BP-Firefly neural networks, which can provide a new perspective for fault diagnosis of coal mining machinery.

Assessment of Socio-Economic Efficiency of the Scientific and Educational Complex: Approach and System of Indicators

The article is devoted to the current topic of choosing the optimal organizational option of multilateral integration of the scientific and educational sphere, business and the state in the process of globalization of the world economy. The authors justify the format of formation of scientific and educational complex on the basis of network interaction, which allows to obtain the greatest synergistic effect. In order to justify the effective network interaction of the scientific and educational complex, an analysis of existing methods of assessing the efficiency of its functioning was carried out and an author’s system of performance indicators and its assessment was proposed in accordance with the general purpose of the integration mechanism and the specific purpose of each interaction subject. The model contains a system of heterogeneous indicators reflecting the principles of formation of a scientific and educational complex on the basis of network interaction, which allows, along with an evaluation task, to determine, using factor models, further directions of inter-network relations of subjects in order to better understand the current processes and identify problem areas of coordination of their innovative activity.

Security and Privacy in Information Management in a Distributed Environment for Public Organizations

Security and privacy problems in information management are evident in public organizations. The objective of this research is the analisys risks that these organizations run, since computer attacks have increased along with both internal and external threats. Causing information and database thefts, there are risk analysis methodologies which are oriented to the objective for the preservation of guaranteeing the security and privacy of the information. Were used the deductive method and exploratory research to analyze the articles in the references and in the information available online and MAGERIT methodology what protects the information in its integrity, confidentiality and availability guaranteeing the security of the system and processes of public organizations. It turned out a Control of Security and Privacy factors, Threat Probability, Risk Assessment Formula, Prototype of Risk Management for Public Organizations and Privacy and security factor formula. It was concluded that MAGERIT is an alternative what allow mitigate the vulnerabilitys, threat and risks its processes in public organizations for protecting their information.

Using the Generative Adversarial Network to Generate Recommendations

Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.

Data-Driven Modeling of Mechanical Properties of Cast Iron Using Fuzzy Logic

For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

LAR: A User Behavior Prediction Model in Server Log Based on LSTM-Attention Network and RSC Algorithm

Using server log data to predict the URLs that a user is likely to visit is an important research area in user behavior prediction. In this paper, a predictive model (called LAR) based on the long short-term memory (LSTM) attention network and reciprocal-nearest-neighbors supported clustering algorithm (RSC) for predicting the URL is proposed. First, the LSTM-attention network is used to predict the URL categories a user might visit, and the RSC algorithm is then used to cluster users. Subsequently, the URLs belonging to the same category are determined from the user clusters to predict the URLs that the user might visit. The proposed LAR model considers the time sequence of the user access URL, and the relationship between a single user and group users, which effectively improves the prediction accuracy. The experimental results demonstrate that the LAR model is feasible and effective for user behavior prediction. The accuracy of the mean absolute error and root mean square error of the LAR model are better than those of the other models compared in this study.