Graph-structural Modeling of Some Special Organizational Systems

Blyumin, Semen; Pogodaev, Anatoly; Khabibullina, Elena

doi:10.1109/summa50634.2020.9280724

Cited by 5 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since there is no detailed solution for the model described, we must resort to approximate techniques for estimating the value of the hyperparameters θ and τ . As a first step, we rewrite the network statistics as a function, not of the edges, but the degree of k i each node [ 33 , 39 , 40 ]:

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Comparing the equations, we observe that they express the same ERGM [ 41 ], i.e., the 2-star model, using different network statistics. The free energy of the model described by the Hamiltonian is calculated as follows [ 40 , 42 , 43 ]:

where φ 0 is the solution of the mean field approach given as follows:

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Therefore, the above equations fully describe the 2-star model in terms of the mean field. Given the original Hamiltonian of the solvable 2-star model, then the following relation is true [ 36 , 40 ]:

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…So given that the element

results from the interior product of the i th row vector of the table W on the j th column vector of H , we arrive at the following relation [ 40 , 44 , 49 ]:

…”

Section: Proposed Methodologymentioning

confidence: 99%

See 3 more Smart Citations

An Advanced Deep Attention Collaborative Mechanism for Secure Educational Email Services

Chen¹,

Yang²

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The COVID-19 crisis has once again highlighted the vulnerabilities of some critical areas in cyberspace, especially in the field of education, as distance learning and social distance have increased their dependence on digital technologies and connectivity. Many recent cyberattacks on e-learning systems, educational content services, and trainee management systems have created severe demands for specialized technological solutions to protect the security of modern training methods. Email is one of the most critical technologies of educational organizations that are attacked daily by spam, phishing campaigns, and all kinds of malicious programs. Considering the efforts made by the global research community to ensure educational processes, this study presents an advanced deep attention collaborative filter for secure academic email services. It is a specialized application of intelligent techniques that, for the first time, examines and models the problem of spam as a system of graphs where collaborative referral systems undertake the processing and analysis of direct and indirect social information to detect and categorize spam emails. In this study, nonnegative matrix factorization (NMF) is applied to the social graph adjacent table to place users in one (or more) overlapping communities. Also, using a deep attention mechanism, it becomes personalized for each user. At the same time, with the introduction of exponential random graph models (ERGMs) in the process of factorization, local dependencies are significantly mitigated to achieve the revelation of malicious communities. This methodology is being tested successfully in implementing mail protection systems for educational organizations. According to the findings, the proposed algorithm outperforms all other compared algorithms in every metric tested.

show abstract

…”

Section: Proposed Methodologymentioning

confidence: 99%

where φ 0 is the solution of the mean field approach given as follows:

…”

Section: Proposed Methodologymentioning

confidence: 99%

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…So given that the element

results from the interior product of the i th row vector of the table W on the j th column vector of H , we arrive at the following relation [ 40 , 44 , 49 ]:

…”

Section: Proposed Methodologymentioning

confidence: 99%

See 2 more Smart Citations

An Advanced Deep Attention Collaborative Mechanism for Secure Educational Email Services

Chen¹,

Yang²

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The Hessian matrix of second-order partial and cross-partial derivatives determines whether or not the likelihood equations indicated root is in fact a (local) maximum [ 21 , 22 ]:

…”

Section: Methodsmentioning

confidence: 99%

Recommendation System for Privacy-Preserving Education Technologies

Yin

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals’ personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users’ privacy.

show abstract