scholarly journals COMPLEX SIMULATION-STATISTICAL METHOD FOR SYNTHESIZING CONDITIONALLY REAL DATA ARRAYS BASED ON A STRUCTURAL-PARAMETRIC MODEL OF INTERACTION BETWEEN USERS OF INFORMATION AND TELECOMMUNICATION SERVICES

2020 ◽  
Vol 20 (1) ◽  
pp. 12-23
Author(s):  
N. А. Gaidamakin ◽  
◽  
N. I. Sinadsky ◽  
P. V. Sushkov ◽  
◽  
...  
Keyword(s):  
Computer Training ◽  
Real Data ◽  
Parametric Model ◽  
Telecommunication Services ◽  
Software Products ◽  
Partial Isomorphism ◽  
Complex Simulation ◽  
Social Graphs ◽  
Structural Event ◽  
Dynamic Components

The article presents a solution to the problem of synthesizing training tasks and data arrays when organizing a computer training platform for conducting practical exercises to investigate information security incidents. Two main stages of the synthesis of the background and situa-tional components of the conditionally real data arrays based on the structural-parametric model of interaction between users of information and telecommunication services are pro-posed: the formation of static and dynamic components. 14КОМПЛЕС НЫЙИ. ТОАИЦ-МЛИМПЧ К ЕЛЙИДЗ-ВЕИЛЛИУ МЙОДО Р 1Ь35Х / 2020Вступление в силу Федерального закона «О безопасности критической информацион-ной инфраструктуры Российской Федера-ции» свидетельствует об актуальности и зна-чимости решения задачи по обнаружению, предупреждению и ликвидации последствий компьютерных атак и реагирования на ком-пьютерные инциденты. Условием качествен-ного решения данных задач является практи-ко-ориентированная профессиональная под-готовка соответствующих специалистов. Для организации и проведения практи-ческих занятий по расследованию инциден-тов информационной безопасности в сетях документальной электросвязи и сети Интер-нет как на потоках магистратуры по направ-лению «Информационная безопасность», так и на потоках специалитета «Информацион-ная безопасность телекоммуникационных систем» и «Информационно-аналитические системы безопасности» необходимо созда-ние учебного компьютерного полигона, осна-щенного современными образцами инфор-мационно-аналитических систем безопасно-сти (далее – ИАСБ), такими как IBM I21, МФИ СОФТ «Январь»2, Lampyre3, Gephi4 и др. ИАСБ – это аппаратно-программные ком-плексы для проведения поисково-аналитиче-ской работы, имеющие возможность нака-пливать и анализировать данные о взаимо-действии пользователей информационно-те-1 http://www.ibm.com/software/ products/ru/analysts-notebook2 http://www.mfisoft.ru/direction/sorm/sorm-3/3 http://www.lampyre.io4 http://www.gephi.orgлекоммуникационных сервисов (далее – ИТ-сервисов). Однако подключение учебных ИАСБ к действующему оборудованию опера-торов связи, являющемуся источником ин-формации о взаимодействии пользователей ИТ-сервисов, невозможно в соответствии со ст. 64 Федерального закона «О связи». Также отсутствует возможность применения насто-ящих массивов биллинговой информации в силу того, что такие массивы содержат персо-нальные данные пользователей, а доступ к ним ограничен законодательно. Обзор литературы позволяет сделать вы-вод об отсутствии готовых методов и алгорит-мов генерации массивов данных, отражающих взаимодействие пользователей ИТ-сервисов.Для решения данной проблемы при соз-дании учебного компьютерного полигона по расследованию инцидентов информацион-ной безопасности в учебно-научном центре «Информационная безопасность» ИРИТ-РтФ УрФУ им. первого Президента России Б.Н. Ельцина разработано программное обеспе-чение (далее – ПО). Данное ПО работает в со-ответствии с созданной структурно-параме-трической моделью взаимодействия пользо-вателей в ИТ-сервисах и позволяет синтези-ровать массивы условно-реальных данных. Файлы, содержащие сгенерированные мас-сивы, в дальнейшем загружаются в базу дан-ных ИАСБ для проведения практических за-нятий по решению поисково-аналитических задач. На текущем этапе разработки ПО спо-собно генерировать массивы условно-реаль-ных данных о взаимодействии пользователей социальных сетей и сетей мобильной связи.Static components are synthesized based on the method of forming the structure of social graphs using a composition of models for constructing complex networks with various struc-tural parameters: the Watts-Strogatz model is used for a mobile communication service, and the Barabashi-Albert model is used for a social network service. To preserve the relationship between users in various services, a method is proposed for determining the largest common part of social graphs, based on the mutual differentiation of vertices and the allocation of a partial isomorphism of the compared graphs. When generating vertex attributes, the method of searching for social groups (families) is used, based on the Bron-Kerbosch algorithm for find-ing a click of a given size in a graph.For the synthesis of dynamic components of data arrays that describe the performance of communication events, the mathematical apparatus of color Petri nets is used. An interaction event in information and telecommunication services is represented in the form of a Petri net label, which contains the necessary set of parameters, depending on the type of service. It is proposed to use the structural, event, social and temporal statistical characterist

scholarly journals A UNIFIED COMMUNITY DETECTION, VISUALIZATION AND ANALYSIS METHOD

2014 ◽  
Vol 17 (01) ◽  
pp. 1450001 ◽  
Author(s):  
MICHEL CRAMPES ◽  
MICHEL PLANTIÉ
Keyword(s):  
Community Detection ◽  
Directed Graphs ◽  
Real Data ◽  
Semantic Interpretation ◽  
Detection Methods ◽  
Analysis Method ◽  
Overlapping Communities ◽  
Social Graphs ◽  
Tractography Data ◽  
Research Domain

With the widespread social networks on the Internet, community detection in social graphs has recently become an important research domain. Interest was initially limited to unipartite graph inputs and partitioned community outputs. More recently, bipartite graphs, directed graphs and overlapping communities have all been investigated. Few contributions however have encompassed all three types of graphs simultaneously. In this paper, we present a method that unifies community detection for these three types of graphs while at the same time it merges partitioned and overlapping communities. Moreover, the results are visualized in a way that allows for analysis and semantic interpretation. For validation purposes this method is experimented on some well-known simple benchmarks and then applied to real data: photos and tags in Facebook and Human Brain Tractography data. This last application leads to the possibility of applying community detection methods to other fields such as data analysis with original enhanced performances.

Reale Daten für Simulationen im digitalen Zwilling*/Real data for simulation in the digital twin -Study on how to record Profinet data and reproduce them in complex simulation environments

2019 ◽  
Vol 109 (09) ◽  
pp. 662-666
Author(s):  
M. Chemnitz ◽  
O. Heimann ◽  
A. Vick
Keyword(s):  
Twin Study ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Real Data ◽  
Control Data ◽  
Cause Analysis ◽  
Digital Twin ◽  
Root Cause ◽  
Complex Simulation ◽  
Complex Manufacturing Systems

Die hohen Anforderungen an moderne Fertigungssysteme erfordern leistungsfähige Engineering-Lösungen. Wie man die Identifikation von Fehlerursachen in komplexen Anlagen erleichtert, wurde in einer Machbarkeitsstudie des Fraunhofer IPK im Auftrag von Siemens DI FA untersucht. In der vorgestellten Lösung werden die Daten der Anlage auf Feldbusebene erfasst und in den digitalen Zwilling eingespeist. So kann das Verhalten der Komponenten taktgenau nachvollzogen werden. Dies elaubt einen tiefen Einblick in das System und unterstützt so bei der Fehlerbehebung.   Powerful engineering tools are required to keep modern production systems manageable. Siemens DI FA and the Fraunhofer IPK present a novel tool for root cause analysis within complex manufacturing systems. The solution combines a CAx plant model with control data recorded from the field bus. This creates a comprehensive digital twin, allowing to analyse past machine behavior with bus clock resolution.

Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions

2013 ◽  
Vol 9 (2) ◽  
Author(s):  
Iván Díaz ◽  
Mark J. van der Laan
Keyword(s):  
Population Distribution ◽  
Causal Effect ◽  
Real Data ◽  
Parametric Model ◽  
Minimum Loss ◽  
Inverse Probability ◽  
Stochastic Nature ◽  
Policy Interventions ◽  
Definition Of ◽  
Model Inverse

AbstractAssessing the causal effect of an exposure often involves the definition of counterfactual outcomes in a hypothetical world in which the stochastic nature of the exposure is modified. Although stochastic interventions are a powerful tool to measure the causal effect of a realistic intervention that intends to alter the population distribution of an exposure, their importance to answer questions about plausible policy interventions has been obscured by the generalized use of deterministic interventions. In this article, we follow the approach described in Díaz and van der Laan (2012) to define and estimate the effect of an intervention that is expected to cause a truncation in the population distribution of the exposure. The observed data parameter that identifies the causal parameter of interest is established, as well as its efficient influence function under the non-parametric model. Inverse probability of treatment weighted (IPTW), augmented IPTW and targeted minimum loss-based estimators (TMLE) are proposed, their consistency and efficiency properties are determined. An extension to longitudinal data structures is presented and its use is demonstrated with a real data example.

Bridging the Gap between LMS and Social Network Learning Analytics in Online Learning

2016 ◽  
Vol 9 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Ángel Hernández-García ◽  
Miguel Ángel Conde-González
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Collaborative Learning ◽  
Learning Analytics ◽  
Real Data ◽  
General Purpose ◽  
Computer Supported Collaborative Learning ◽  
Message Boards ◽  
Social Graphs

Despite the great potential of social network analysis (SNA) methods and visualizations for learning analytics in computer-supported collaborative learning (CSCL), these approaches have not been fully explored due to two important barriers: the scarcity and limited functionality of built-in tools in Learning Management Systems (LMS), and the difficulty to import educational data from formal virtual learning environments into social network analysis programs. This study aims to cover that gap by introducing GraphFES, an application and web service for extraction of interaction data from Moodle message boards and generation of the corresponding social graphs for later analysis using Gephi, a general purpose SNA software. In addition, this paper briefly illustrates the potential of the combination of the three systems (Moodle, GraphFES and Gephi) for social learning analytics using real data from a computer-supported collaborative learning course with strong focus on teamwork and intensive use of forums.

Embedded Model Problem

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Model Problem ◽  
Model Fitting ◽  
Regression Function ◽  
Real Data ◽  
Parametric Model ◽  
Straight Line ◽  
True Model ◽  
Embedded Model ◽  
Special Case ◽  
Two Parameter

This chapter introduces embedded models. This is a special case of a parametric model which cannot be obtained simply by setting the parameters to particular values in a simple way. An example is the regression function y = b[1−exp(−ax)], which is always curved when a and b have fixed values. But letting a tend to zero and b tend to infinity simultaneously, whilst keeping ab = c fixed, yields y = cx, a straight-line special case. When this is the true model, fitting the original two-parameter model leads to very unstable and individually meaningless estimates of a and b. Such embedded models are actually very common in the literature, leading to confusion in interpretation of results when undetected. In this chapter, embeddedness is defined and a large number of regression embedded model examples given. Detection and removal of embeddedness by reparametrization is discussed. Two real data numerical examples are given.

scholarly journals The Three-parameters Marshall-Olkin Generalized Weibull Model with Properties and Different Applications to Real Data Sets

2020 ◽  
pp. 675-688
Author(s):  
Mohamed G. Khalil ◽  
Wagdy M. Kamel
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Real Data ◽  
Weibull Model ◽  
Parametric Model ◽  
Data Sets ◽  
Unknown Parameters ◽  
Graphical Simulation ◽  
Method Of Maximum Likelihood ◽  
New Distribution

A new three-parameter life parametric model called the Marshall-Olkin generalized Weibull is defined and studied. Relevant properties are mathematically derived and analyzed. The new density exhibits various important symmetric and asymmetric shapes with different useful kurtosis. The new failure rate can be “constant”, “upside down-constant (reversed U-HRF-constant)”, “increasing then constant”, “monotonically increasing”, “J-HRF” and “monotonically decreasing”. The method of maximum likelihood is employed to estimate the unknown parameters. A graphical simulation is performed to assess the performance of the maximum likelihood estimation. We checked and proved empirically the importance, applicability and flexibility of the new Weibull model in modeling various symmetric and asymmetric types of data. The new distribution has a high ability to model different symmetric and asymmetric types of data.

scholarly journals Development of calculation parametric model labor and cost of software products based on high-level language programming

2012 ◽  
Vol 5 (2(7)) ◽  
pp. 15-16
Author(s):  
Виктория Олеговна Бортникова
Keyword(s):  
Parametric Model ◽  
High Level Language ◽  
Software Products ◽  
High Level

scholarly journals Identifiability of a Markovian model of molecular evolution with gamma-distributed rates

2008 ◽  
Vol 40 (01) ◽  
pp. 229-249 ◽  
Author(s):  
Elizabeth S. Allman ◽  
Cécile Ané ◽  
John A. Rhodes
Keyword(s):  
Markov Models ◽  
Continuous Distribution ◽  
Real Data ◽  
Parametric Model ◽  
Evolutionary Trees ◽  
Data Sets ◽  
Biological Sequences ◽  
Markovian Model ◽  
Rate Heterogeneity ◽  
Phylogenetic Model

Inference of evolutionary trees and rates from biological sequences is commonly performed using continuous-time Markov models of character change. The Markov process evolves along an unknown tree while observations arise only from the tips of the tree. Rate heterogeneity is present in most real data sets and is accounted for by the use of flexible mixture models where each site is allowed its own rate. Very little has been rigorously established concerning the identifiability of the models currently in common use in data analysis, although nonidentifiability was proven for a semiparametric model and an incorrect proof of identifiability was published for a general parametric model (GTR + Γ + I). Here we prove that one of the most widely used models (GTR + Γ) is identifiable for generic parameters, and for all parameter choices in the case of four-state (DNA) models. This is the first proof of identifiability of a phylogenetic model with a continuous distribution of rates.

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