This document proposes modifications to Pawlak's conflict theory model based on graph theory. It suggests developing the conflict analysis system to predict how the opinions of neutral agents may change over time. The approach involves:
1) Creating matrices to represent direct conflicts, alliances, and neutral relationships between agents.
2) Computing higher power matrices through multiplication to represent indirect relationships over increasing path lengths.
3) Weighting the matrices based on path length and summing values to predict if neutral relationships may become conflicts or alliances based on direct and indirect influences.
4) Optionally performing logical OR operations on conflict matrices to identify any direct or indirect conflicts between agents.
(δ,l)-diversity: Privacy Preservation for Publication Numerical Sensitive Data cscpconf
(ε,m)-anonymity considers ε as the interval to define similarity between two values, and m as
the level of privacy protection. For example {40,60} satisfies (ε,m)-anonymity but {40,50,60}
doesn't, for ε=15 and m=2. We show that protection in {40,50,60} sensitive values of an equivalence class is not less (if don't say more) than {40,60}. Therefore, although (ε,m)-anonymity has well studied publication of numerical sensitive values, it fails to address proximity in the right way. Accordingly, we introduce a revised principle which solve this problem by introducing (δ,l)-diversity principle. Surprisingly, in contrast with (ε,m)-anonymity, the proposed principle respects monotonicity property which makes it adoptable to be exploited in other anonymity principles
Binary dependent variable classification model in context of large databases: interpretation via visual tools such as partial dependency plots for 1, 2, 3, and 4 variables and other plots. Presentation focuses on overall and not individual observation interpretation, and is still work in progress.
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing non-isosceles TFN when we are using direct rating for evaluating a variable in a predefined scale. In this method, the relevancy between assessment uncertainties and statistical parameters such as mean value and the standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
Unsteady MHD Flow Past A Semi-Infinite Vertical Plate With Heat Source/ Sink:...IJERA Editor
In the present paper a numerical attempt is made to study the combined effects of heat source and sink on unsteady laminar boundary layer flow of a viscous, incompressible, electrically conducting fluid along a semiinfinite vertical plate. A magnetic field of uniform strength is applied normal to the flow. The governing boundary layer equations are solved numerically, using Crank-Nicolson method. Graphical results of velocity and temperature fields, tabular values of Skin-friction and Nusselt are presented and discussed at various parametric conditions. From this study, it is found that the velocity and temperature of the fluid increase in the presence of heat source but they decrease in the presence of heat absorption parameter.
(δ,l)-diversity: Privacy Preservation for Publication Numerical Sensitive Data cscpconf
(ε,m)-anonymity considers ε as the interval to define similarity between two values, and m as
the level of privacy protection. For example {40,60} satisfies (ε,m)-anonymity but {40,50,60}
doesn't, for ε=15 and m=2. We show that protection in {40,50,60} sensitive values of an equivalence class is not less (if don't say more) than {40,60}. Therefore, although (ε,m)-anonymity has well studied publication of numerical sensitive values, it fails to address proximity in the right way. Accordingly, we introduce a revised principle which solve this problem by introducing (δ,l)-diversity principle. Surprisingly, in contrast with (ε,m)-anonymity, the proposed principle respects monotonicity property which makes it adoptable to be exploited in other anonymity principles
Binary dependent variable classification model in context of large databases: interpretation via visual tools such as partial dependency plots for 1, 2, 3, and 4 variables and other plots. Presentation focuses on overall and not individual observation interpretation, and is still work in progress.
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing non-isosceles TFN when we are using direct rating for evaluating a variable in a predefined scale. In this method, the relevancy between assessment uncertainties and statistical parameters such as mean value and the standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
Unsteady MHD Flow Past A Semi-Infinite Vertical Plate With Heat Source/ Sink:...IJERA Editor
In the present paper a numerical attempt is made to study the combined effects of heat source and sink on unsteady laminar boundary layer flow of a viscous, incompressible, electrically conducting fluid along a semiinfinite vertical plate. A magnetic field of uniform strength is applied normal to the flow. The governing boundary layer equations are solved numerically, using Crank-Nicolson method. Graphical results of velocity and temperature fields, tabular values of Skin-friction and Nusselt are presented and discussed at various parametric conditions. From this study, it is found that the velocity and temperature of the fluid increase in the presence of heat source but they decrease in the presence of heat absorption parameter.
Operations research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved in defined steps by mathematical analysis.
Analytical methods used in OR include mathematical logic, simulation, network analysis, queuing theory , and game theory .The process can be broadly broken down into three steps.
1. A set of potential solutions to a problem is developed. (This set may be large.)
2. The alternatives derived in the first step are analyzed and reduced to a small set of solutions most likely to prove workable.
3. The alternatives derived in the second step are subjected to simulated implementation and, if possible, tested out in real-world situations. In this final step, psychology and management science often play important roles
In this paper we focus on mixed model analysis for regression model to take account of over dispersion in random effects. Moreover, we present the Data Exploration, Box plot, QQ plot, Analysis of variance, linear models, linear mixed –effects model for testing the over dispersion parameter in the mixed model. A mixed model is similar in many ways to a linear model. It estimates the effects of one or more explanatory variables on a response variable. In this article, the mixed model analysis was analyzed with the R-Language. The output of a mixed model will give you a list of explanatory values, estimates and confidence intervals of their effect sizes, P-values for each effect, and at least one measure of how well the model fits. The application of the model was tested using open-source dataset such as using numerical illustration and real datasets
Literature Review on Vague Set Theory in Different Domainsrahulmonikasharma
Problem of decision making is a crucial task in every business. This decision making job is found very difficult when it is depends on the imprecise and vague environment, which is frequent in recent years. Vague sets are an extension of Fuzzy sets. In the fuzzy sets, each object is assigned a single value in the interval [0,1] reflecting its grade of membership. This single value does not allow a separation of evidence for membership and evidence against membership. Gau et al. proposed the notion of vague sets, where each object is characterized by two different membership functions: a true membership function and a false membership function. This kind of reasoning is also called interval membership, as opposed to point membership in the context of fuzzy sets. In this paper, reviews the related works on the decision making by using vague sets in different fields.
Collocation Extraction Performance Ratings Using Fuzzy logicWaqas Tariq
The performance of Collocation extraction cannot quantified or properly express by a single dimension. It is very imprecise to interpret collocation extraction metrics without knowing what application (users) are involved. Most of the existing collocation extraction techniques are of Berry-Roughe, Church and Hanks, Kita, Shimohata, Blaheta and Johnson, and Pearce. The extraction techniques need to be frequently updated based on feedbacks from implementation of previous policies. These feedbacks are always stated in the form of ordinal ratings, e.g. “high speed”, “average performance”, “good condition”. Different people can describe different values to these ordinal ratings without a clear-cut reason or scientific basis. There is need for a way or means to transform vague ordinal ratings to more appreciable and precise numerical estimates. The paper transforms the ordinal performance ratings of some Collocation performance techniques to numerical ratings using Fuzzy logic. Keywords: Fuzzy Set Theory, collocation extraction, Transformation, performance Techniques, Criteria.
TYPE-2 FUZZY LINEAR PROGRAMMING PROBLEMS WITH PERFECTLY NORMAL INTERVAL TYPE-...ijceronline
In this paper, the Perfectly normal Interval Type-2 Fuzzy Linear Programming (PnIT2FLP) model is considered. This model is reduced to crisp linear programming model. This transformation is performed by a proposed ranking method. Based on the proposed fuzzy ranking method and arithmetic operation, the solution of Perfectly normal Interval Type-2 Fuzzy Linear Programming model is obtained by the solutions of linear programming model with help of MATLAB. Finally, the method is illustrated by numerical examples.
The concept of an intuitionistic fuzzy number (IFN) is of importance for representing an ill-known quantity. Ranking fuzzy numbers plays a very important role in the decision process, data analysis and applications. The concept of an IFN is of importance for quantifying an ill-known quantity. Ranking of intuitionistic fuzzy numbers plays a vital role in decision making and linear programming problems. Also, ranking of intuitionistic fuzzy numbers is a very difficult problem. In this paper, a new method for ranking intuitionistic fuzzy number is developed by means of magnitude for different forms of intuitionistic fuzzy numbers. In Particular ranking is done for trapezoidal intuitionistic fuzzy numbers, triangular intuitionistic fuzzy numbers, symmetric trapezoidal intuitionistic fuzzy numbers, and symmetric triangular intuitionistic fuzzy numbers. Numerical examples are illustrated for all the defined different forms of intuitionistic fuzzy numbers. Finally some comparative numerical examples are illustrated to express the advantage of the proposed method.
Event Coreference Resolution using Mincut based Graph Clustering cscpconf
To extract participants of an event instance, it is necessary to identify all the sentences that
describe the event instance. The set of all sentences referring to the same event instance are said
to be corefering each other. Our proposed approach formulates the event coreference resolution
as a graph based clustering model. It identifies the corefering sentences using minimum cut
(mincut) based on similarity score between each pair of sentences at various levels such as
trigger word similarity, time stamp similarity, entity similarity and semantic similarity. It
achieves good B-Cubed F-measure score with some loss in recall.
ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRET...ijsc
In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers
are commonly performed. Aggregation and defuzzification operations are some of these often used
operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s
strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take
into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the
WABL operator and, through machine learning, can be trained in the direction of the decision maker's
strategy, producing more satisfactory results for the decision maker. However, in order to determine the
WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete
trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the
calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy
numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers
have been studied. Computational examples explaining the theoretical results have been performed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
COMPARISON OF DIFFERENT APPROXIMATIONS OF FUZZY NUMBERSWireilla
ABSTRACT
The notions of interval approximations of fuzzy numbers and trapezoidal approximations of fuzzy numbers have been discussed. Comparisons have been made between the close-interval approximation, valueambiguity interval approximation and distinct approximation with the corresponding crisp and trapezoidal fuzzy numbers. A numerical example is included to justify the above mentioned notions.
Operations research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved in defined steps by mathematical analysis.
Analytical methods used in OR include mathematical logic, simulation, network analysis, queuing theory , and game theory .The process can be broadly broken down into three steps.
1. A set of potential solutions to a problem is developed. (This set may be large.)
2. The alternatives derived in the first step are analyzed and reduced to a small set of solutions most likely to prove workable.
3. The alternatives derived in the second step are subjected to simulated implementation and, if possible, tested out in real-world situations. In this final step, psychology and management science often play important roles
In this paper we focus on mixed model analysis for regression model to take account of over dispersion in random effects. Moreover, we present the Data Exploration, Box plot, QQ plot, Analysis of variance, linear models, linear mixed –effects model for testing the over dispersion parameter in the mixed model. A mixed model is similar in many ways to a linear model. It estimates the effects of one or more explanatory variables on a response variable. In this article, the mixed model analysis was analyzed with the R-Language. The output of a mixed model will give you a list of explanatory values, estimates and confidence intervals of their effect sizes, P-values for each effect, and at least one measure of how well the model fits. The application of the model was tested using open-source dataset such as using numerical illustration and real datasets
Literature Review on Vague Set Theory in Different Domainsrahulmonikasharma
Problem of decision making is a crucial task in every business. This decision making job is found very difficult when it is depends on the imprecise and vague environment, which is frequent in recent years. Vague sets are an extension of Fuzzy sets. In the fuzzy sets, each object is assigned a single value in the interval [0,1] reflecting its grade of membership. This single value does not allow a separation of evidence for membership and evidence against membership. Gau et al. proposed the notion of vague sets, where each object is characterized by two different membership functions: a true membership function and a false membership function. This kind of reasoning is also called interval membership, as opposed to point membership in the context of fuzzy sets. In this paper, reviews the related works on the decision making by using vague sets in different fields.
Collocation Extraction Performance Ratings Using Fuzzy logicWaqas Tariq
The performance of Collocation extraction cannot quantified or properly express by a single dimension. It is very imprecise to interpret collocation extraction metrics without knowing what application (users) are involved. Most of the existing collocation extraction techniques are of Berry-Roughe, Church and Hanks, Kita, Shimohata, Blaheta and Johnson, and Pearce. The extraction techniques need to be frequently updated based on feedbacks from implementation of previous policies. These feedbacks are always stated in the form of ordinal ratings, e.g. “high speed”, “average performance”, “good condition”. Different people can describe different values to these ordinal ratings without a clear-cut reason or scientific basis. There is need for a way or means to transform vague ordinal ratings to more appreciable and precise numerical estimates. The paper transforms the ordinal performance ratings of some Collocation performance techniques to numerical ratings using Fuzzy logic. Keywords: Fuzzy Set Theory, collocation extraction, Transformation, performance Techniques, Criteria.
TYPE-2 FUZZY LINEAR PROGRAMMING PROBLEMS WITH PERFECTLY NORMAL INTERVAL TYPE-...ijceronline
In this paper, the Perfectly normal Interval Type-2 Fuzzy Linear Programming (PnIT2FLP) model is considered. This model is reduced to crisp linear programming model. This transformation is performed by a proposed ranking method. Based on the proposed fuzzy ranking method and arithmetic operation, the solution of Perfectly normal Interval Type-2 Fuzzy Linear Programming model is obtained by the solutions of linear programming model with help of MATLAB. Finally, the method is illustrated by numerical examples.
The concept of an intuitionistic fuzzy number (IFN) is of importance for representing an ill-known quantity. Ranking fuzzy numbers plays a very important role in the decision process, data analysis and applications. The concept of an IFN is of importance for quantifying an ill-known quantity. Ranking of intuitionistic fuzzy numbers plays a vital role in decision making and linear programming problems. Also, ranking of intuitionistic fuzzy numbers is a very difficult problem. In this paper, a new method for ranking intuitionistic fuzzy number is developed by means of magnitude for different forms of intuitionistic fuzzy numbers. In Particular ranking is done for trapezoidal intuitionistic fuzzy numbers, triangular intuitionistic fuzzy numbers, symmetric trapezoidal intuitionistic fuzzy numbers, and symmetric triangular intuitionistic fuzzy numbers. Numerical examples are illustrated for all the defined different forms of intuitionistic fuzzy numbers. Finally some comparative numerical examples are illustrated to express the advantage of the proposed method.
Event Coreference Resolution using Mincut based Graph Clustering cscpconf
To extract participants of an event instance, it is necessary to identify all the sentences that
describe the event instance. The set of all sentences referring to the same event instance are said
to be corefering each other. Our proposed approach formulates the event coreference resolution
as a graph based clustering model. It identifies the corefering sentences using minimum cut
(mincut) based on similarity score between each pair of sentences at various levels such as
trigger word similarity, time stamp similarity, entity similarity and semantic similarity. It
achieves good B-Cubed F-measure score with some loss in recall.
ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRET...ijsc
In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers
are commonly performed. Aggregation and defuzzification operations are some of these often used
operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s
strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take
into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the
WABL operator and, through machine learning, can be trained in the direction of the decision maker's
strategy, producing more satisfactory results for the decision maker. However, in order to determine the
WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete
trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the
calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy
numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers
have been studied. Computational examples explaining the theoretical results have been performed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
COMPARISON OF DIFFERENT APPROXIMATIONS OF FUZZY NUMBERSWireilla
ABSTRACT
The notions of interval approximations of fuzzy numbers and trapezoidal approximations of fuzzy numbers have been discussed. Comparisons have been made between the close-interval approximation, valueambiguity interval approximation and distinct approximation with the corresponding crisp and trapezoidal fuzzy numbers. A numerical example is included to justify the above mentioned notions.
COMPARISON OF DIFFERENT APPROXIMATIONS OF FUZZY NUMBERSijfls
The notions of interval approximations of fuzzy numbers and trapezoidal approximations of fuzzy numbers have been discussed. Comparisons have been made between the close-interval approximation, valueambiguity
interval approximation and distinct approximation with the corresponding crisp and trapezoidal fuzzy numbers. A numerical example is included to justify the above mentioned notions.
Today’s market evolution and high volatility of business requirements put an increasing emphasis on the
ability for systems to accommodate the changes required by new organizational needs while maintaining
security objectives satisfiability. This is all the more true in case of collaboration and interoperability
between different organizations and thus between their information systems. Ontology mapping has been
used for interoperability and several mapping systems have evolved to support the same. Usual solutions
do not take care of security. That is almost all systems do a mapping of ontologies which are unsecured.
We have developed a system for mapping secured ontologies using graph similarity concept.
Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Sy...gerogepatton
Research on explanation is currently of intense interest as documented in the DARPA 2021 investments
reported by the USA Department of Defense. An emerging theme for explanation techniques research is
their application to the improvement of human-system interfaces for autonomous anti-drone or C-UAV
defense systems. In the present paper a novel proposal based on natural language processing technology
concerning explanatory discourse using relations is briefly described. The proposal is based on the use of
relations pertaining to the possible malicious actions of an intruding alien drone swarm and the defense
decisions proposed by an autonomous anti-drone system. The aim of such an interface is to facilitate the
supervision that a user must exercise on an autonomous defense system in order to minimize the risk of
wrong mitigation actions and unnecessary spending of ammunition.
HUMAN-SYSTEM INTERFACE WITH EXPLANATION OF ACTIONS FOR AUTONOMOUS ANTI-UAV SY...ijaia
Research on explanation is currently of intense interest as documented in the DARPA 2021 investments
reported by the USA Department of Defense. An emerging theme for explanation techniques research is
their application to the improvement of human-system interfaces for autonomous anti-drone or C-UAV
defense systems. In the present paper a novel proposal based on natural language processing technology
concerning explanatory discourse using relations is briefly described. The proposal is based on the use of
relations pertaining to the possible malicious actions of an intruding alien drone swarm and the defense
decisions proposed by an autonomous anti-drone system. The aim of such an interface is to facilitate the
supervision that a user must exercise on an autonomous defense system in order to minimize the risk of
wrong mitigation actions and unnecessary spending of ammunition.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
AN IMPLEMENTATION, EMPIRICAL EVALUATION AND PROPOSED IMPROVEMENT FOR BIDIRECT...ijaia
Abstract argumentation frameworks are formal systems that facilitate obtaining conclusions from nonmonotonic knowledge systems. Within such a system, an argumentation semantics is defined as a set of arguments with some desired qualities, for example, that the elements are not in conflict with each other.
Splitting an argumentation framework can efficiently speed up the computation of argumentation semantics. With respect to stable semantics, two methods have been proposed to split an argumentation framework either in a unidirectional or bidirectional fashion. The advantage of bidirectional splitting is
that it is not structure-dependent and, unlike unidirectional splitting, it can be used for frameworks consisting of a single strongly connected component. Bidirectional splitting makes use of a minimum cut. In this paper, we implement and test the performance of the bidirectional splitting method, along with two
types of graph cut algorithms. Experimental data suggest that using a minimum cut will not improve the performance of computing stable semantics in most cases. Hence, instead of a minimum cut, we propose to use a balanced cut, where the framework is split into two sub-frameworks of equal size. Experimental results conducted on bidirectional splitting using the balanced cut show a significant improvement in the performance of computing semantics.
A NEW APPROACH FOR RANKING SHADOWED FUZZY NUMBERS AND ITS APPLICATIONijcsit
In many decision situations, decision-makers face a kind of complex problems. In these decision-making
problems, different types of fuzzy numbers are defined and, have multiple types of membership functions.
So, we need a standard form to formulate uncertain numbers in the problem. Shadowed fuzzy numbers are
considered granule numbers which approximate different types and different forms of fuzzy numbers. In
this paper, a new ranking approach for shadowed fuzzy numbers is developed using value, ambiguity and
fuzziness for shadowed fuzzy numbers. The new ranking method has been compared with other existing
approaches through numerical examples. Also, the new method is applied to a hybrid multi-attribute
decision making problem in which the evaluations of alternatives are expressed with different types of
uncertain numbers. The comparative study for the results of different examples illustrates the reliability of
the new method.
In many decision situations, decision-makers face a kind of complex problems. In these decision-making problems, different types of fuzzy numbers are defined and, have multiple types of membership functions. So, we need a standard form to formulate uncertain numbers in the problem. Shadowed fuzzy numbers are considered granule numbers which approximate different types and different forms of fuzzy numbers. In this paper, a new ranking approach for shadowed fuzzy numbers is developed using value, ambiguity and fuzziness for shadowed fuzzy numbers. The new ranking method has been compared with other existing approaches through numerical examples. Also, the new method is applied to a hybrid multi-attribute decision making problem in which the evaluations of alternatives are expressed with different types of uncertain numbers. The comparative study for the results of different examples illustrates the reliability of the new method.
A Game theoretic approach for competition over visibility in social networksjournalBEEI
Social Networks have known an important evolution in the last few years. These structures, made up of individuals who are tied by one or more specific types of interdependency, constitute the window for members to express their opinions and thoughts by sending posts to their own walls or others' timelines. Actually, when a content arrives, it's located on the top of the timeline pushing away older messages. This situation causes a permanent competition over visibility among subscribers who jump on opponents to promote conflict. Our study presents this competition as a non-cooperative game; each source has to choose frequencies which assure its visibility. We model it, exploring the theory of concave games, to reach a situation of equilibrium; a situation where no player has the ultimate ability to deviate from its current strategy. We formulate the named game, then we analyze it and prove that there is exactly one Nash equilibrium which is the convergence of all players' best responses. We finally provide some numerical results, taking into consideration a system of two sources with a specific frequency space, and analyze the effect of different parameters on sources' visibility on the walls of social networks.
A Study on Youth Violence and Aggression using DEMATEL with FCM Methodsijdmtaiir
The DEMATEL method is then a good technique for
making decisions. In this paper we analyzed the risk factors of
youth violence and what makes them more aggressive. Since
there are more risk factors of youth violence, to relate each
other more complex to construct FCM and analyze them.
Moreover the data is an unsupervised one obtained from
survey as well as interviews. Hence fuzzy alone has the
capacity to analyses these concepts.
A New Approach for Ranking Shadowed Fuzzy Numbers and its Application IJCSITJournal2
n many decision situations, decision-makers face a kind of complex problems. In these decision-making
problems, different types of fuzzy numbers are defined and, have multiple types of membership functions.
So, we need a standard form to formulate uncertain numbers in the problem. Shadowed fuzzy numbers are
considered granule numbers which approximate different types and different forms of fuzzy numbers. In
this paper, a new ranking approach for shadowed fuzzy numbers is developed using value, ambiguity and
fuzziness for shadowed fuzzy numbers. The new ranking method has been compared with other existing
approaches through numerical examples. Also, the new method is applied to a hybrid multi-attribute
decision making problem in which the evaluations of alternatives are expressed with different types of
uncertain numbers. The comparative study for the results of different examples illustrates the reliability of
the new method.
Soft Computing: Contents, Techniques and ApplicationCSEIJJournal
Soft Computing is a relatively new branch of Computer Science that deals with approximate reasoning. The
techniques of Soft Computing are used successfully nowadays in many domestic, commercial and industrial
applications becoming a major research object in automatic control engineering. The present paper
reviews the contents of Soft Computing, which include probabilistic and in particular Bayesian reasoning,
fuzzy logic, artificial neural networks and genetic algorithms. These topics are complementary to each
other and can be used simultaneously for solving complex real-life problems, which cannot or it is too
difficult be modelled mathematically. The paper also explores the main techniques used in Soft Computing
and discusses their advantages with respect to the traditional techniques of hard computing
Soft Computing: Contents, Techniques and ApplicationCSEIJJournal
Soft Computing is a relatively new branch of Computer Science that deals with approximate reasoning. The
techniques of Soft Computing are used successfully nowadays in many domestic, commercial and industrial
applications becoming a major research object in automatic control engineering. The present paper
reviews the contents of Soft Computing, which include probabilistic and in particular Bayesian reasoning,
fuzzy logic, artificial neural networks and genetic algorithms. These topics are complementary to each
other and can be used simultaneously for solving complex real-life problems, which cannot or it is too
difficult be modelled mathematically. The paper also explores the main techniques used in Soft Computing
and discusses their advantages with respect to the traditional techniques of hard computing.
Similar to Prediction of Changes That May Occur in the Neutral Cases in Conflict Theory Based on Graph Theory (20)
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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Bob Boule
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Gopinath Rebala
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"Impact of front-end architecture on development cost", Viktor Turskyi
Prediction of Changes That May Occur in the Neutral Cases in Conflict Theory Based on Graph Theory
1. Prediction of Changes That May Occur in the
Neutral Cases in Conflict Theory Based on
Graph Theory
Prof. Dr. Hussein K. Khafaji
AL-Rafidain University College/Computer Communication Eng.Dept
dr.hkm1811@yahoo.com
Huda Ahmed Abed
Iraqi Commission for Computers and Informatics/ Informatics Institute for Postgraduate
Studies
Programer8039@gmail.com
Abstract-Rough set theory is a novel mathematical tool to process uncertainty decision-making problem. It offers a
new viewpoint to study conflict analysis decision making as in Pawlak conflict analysis model.
Conflict Theory supports the political defacto such as the well-known sayings "friend of my friend is my
friend", and "enemy of my enemy is my friend", according to the feature coalition relation in Pawlak conflict theory,
it is possible to expect of indirect relationships between neutral agents based on their relationships with others. There
is no real research dedicated to implement or discuss these features.
In this paper, we attempt to develop the conflict analysis system to predict the changes that may happen in
coalitions and conflicts relations among the agents. These changes usually occur with the neutral agents, they may
change their opinions to coalition or conflict. The proposed modification of conflict model depends on suggested
operations accomplished on the graph representation of the information system, such as ORing, ANDing, XORing,
and finding the indirect coalition and conflict paths among the agents in the model.
Keywords- Conflict analysis, Rough sets theory, Conflict model.
I. Introduction
Conflict is a feature of human nature, which exists in a various situation of life. The goal of studying
conflict is to find the conflicting parties, which have an influence on the decision making. Then try to find a way
to improve the relationship between these conflicting parties [1]. So conflict has been used in various
remarkable fields like trade, economical, governmental and political contention, games, and management
negotiations, military attacks etc., especially in areas that require decision making that have uncertainty
problems. Conflict analysis which goals to find out the kind of conflict has lately attracted raised attention[2]
[3]. Rough set theory is an influential tool in treatment vague information in conflict analysis.
Generally, uncertainty in conflict situation exists in three binary relations between the objects (agents).
These relations can be classified into the coalition, neutrality, and conflict among agents[4][5].
The heart of RST is the concept of indiscernibility relation; therefore, the conflict relation is the
differing or negation of this concept. Its meaning is the discernibility relation. Thus in conflict analysis study, it
is possible to use the conflict relation which is reasonably related to indiscernibility relation [6][7].
II. PAWLAK CONFLICT THEORY
The state of conflict consists of agents, who are in struggle over particular issues. These agents may be
members of parliament, individuals within a company, or any type of agent which have influence on decision-
making. Rough sets are considered fantastic for establishing conflict model. Agents give their opinions
according to the issues raised [8].
Conflict theory can be represented by means of the matrix, where each row is considered as an agent,
while column represent issue understudy. The value of this matrix comprises opinions of agents to specific issue
restricted to one of three values: −1, 0, 1 which means disagreement, neutral, and agreement to the issue
respectively[6][7] [9] [10].
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2. This matrix can be considered as an information system, IS = (Ag, Iu), which encloses two finite nonempty sets
of agents, Ag, and issues Iu, respectively. Iu is an issue set, and the set of potential values of ∈ is plain-
possible-value-of-i={against, neutral, favorable}, representing agent’s opinion, about debated issue, and
mathematically represented as V = {−1, 0, 1} or shortly V = {−, 0, +}.
v (ag, i) is a function returning the value of the opinion of agent about the issue , where ag ∈
Ag, ∈ . For ∈ , a function ( , , ): × → {−1, 0, 1}, which is as follows:
Where and ∈ Ag and ≠ , while ∈ .Three relations are distinct to Ag × Ag: alliance,
neutrality, conflict,
these relations represent to the relationships between agents:
- ( , ) ( , , ) = 1,
- ( , ) ( , , ) = 0,
- ( , ) ( , , ) = −1.
Each of above relation have its own features. First the alliance relation has the following features:
(1) ( , ),
(2) ( , ) ( , ),
(3) ( , ) ( , ) ( , ),
The last condition(3) can be meant as a phrase : "friend of my friend is my friend".
The features of conflict relation can be simplified as:
(4) ( , ),
(5) ( , ) ( , ),
(6) ( , ) ( , ) ( , ),
Property in (6) translate to the famous phrase "enemy of my enemy is my friend"
By the same token the neutrality relationship has the followeing features:
(8) ( , )
(9) ( , ) = ( , ) [51].
The concept of a discernibility matrix assume = ( , ), ⊆ , meant MINFS (Isu), or
M(Isu), it would mean , = | |, matrix represents as follow:
( , ) = {i ∈ Isu|i(r) ≠ i(s)} … … … … … … … . eq.2
So Is(a,b) means all attributes that distinguish agent from .
Every pair of agents and that specify by the discernibility matrix ( ) are sub-set of attributes
( , ) ⊆ , and have features [6][7] [9] [10]:
i. ( , ) = ∅,
ii. ( , ) = ( , ),
iii. ( , ) ⊆ ( , ) ( , ).
now define a conflict function based on discernibility matrix.
CON ( , ) =
| ( , )|
| |
where 0 ≤ ( , ) ≤ 1 … … … … … … eq. 3
- CON ≠ 0, indicates that and in conflict over (issues) with a degree CON ( , )
- ( , ) = 0, indicates that and in coalition about .
The distance function can be represents as: (r, s) iff CON (r, s) > 0.
If (r, s) this means that and are in conflict with degree ( , ).
The calculating of used function *, Instead of function CON.
So a distance function between agents con∗
: Ag × Ag → [0, 1] is clarified:
( , , ) =
1 ( , ) × ( , ) = 1 = ,
0 ( , ) × ( , ) = 0 ≠ ,
−1 ( , ) × ( , ) = −1.
… … … . . eq. 1
International Journal of Computer Science and Information Security (IJCSIS),
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3. con∗ ( , ) =
∑ ∗
(r, s, i) ∈
{ }
… … … … … … … .4
Where:
∗(r, s, i) =
1 − ( , , )
2
… … … … … … … .5
The ∗(r, s, i) based on value auxiliary function ( , , ) to obtain:
=
So the definition of the relations between agents would be clarified as a pair , ∈ that [16] [136] [47]
[87]:
- If ( , ) < 0.5, ℎ ( , ),
- If ( , ) > 0.5, ℎ ( , ),
- If ( , = 0.5, ℎ ( , ).
In many levels of processing, the information system can be represented as digraph, bipartite graph, or
weighted graph, and in this way, it can be obeyed to the graph mathematics, theories, and operations that can be
applied on the graph. In this research, many graph operations are suggested such as ORing, ANDing, and
XORing of the graphs that represent a conflict models according to selected issues. Also, an algorithm is
presented to find the indirect coalition or conflict paths among the agents that plays the main role to predict the
changes that may happened in the opinion of neutral agent. The next section explains the suggested development
that accomplished on the conflict theory.
III. MODIFIED CONFLICT MODEL
Rough set and conflict theory discover knowledge of conflict and alliances for current situation of
agents depending on the interest issues. However, Conflict Theory, supports the political in fact such as the
well-known sayings "friend of my friend is my friend", and "enemy of my enemy is my friend", According to
the feature coalition relation in Pawlak conflict theory:
− ( , ) ( , ) ( , ),
And the features of conflict relation:
− ( , ) ( , ) ( , ),
Therfore it is possible to foresee of indirect relationships between neutral agents based on their relationships
with others.
There is no real application for these features. In this section, an attempt to develop the conflict analysis
system to predict the changes may happen in coalitions and conflicts relations among the agents. These changes
usually occur with the neutral agents, they may alter their situation from coalition to conflict and vice versa.
Neutral agent may alter his opinion by the influence of his direct and/or indirect friends, (direct and/or indirect
alliances) and the behavior of his direct and/or indirect enemies, (direct and/or indirect conflicts).
Remember that, Distance Function matrix DF, or its variations contain three relation, such that DF [A1]
[A2] =0 means A1 and A2 have same opinion about a specific issue(s), while DF [A1] [A2] =1 means A1 and
A2 have different opinions about a specific issue(s). DF [A1] [A2] =0.5 means that at least A1 and/or A2
have/has no opinion about a specific issue(s). The following strategy has been suggested to predict the possible
changes that may occur in agents' opinion.
After the construction of distance function for all issues, it will be utilized for analysis in terms of conflict
by changing all the values of conflict in this function to 1 (values greater than 0.5), while all the remaining
values change to 0 (by neglecting the value of the alliance and neutrality situations). In other word copy the
conflict values of DF in distance function of conflict (DFC) and replace these values by 1, i.e., create binary
matrix. In the same manner, Binary matrix distance function for alliances (DFA) is created from alliance values
0 ( , ) × ( , ) = 1 = , ( , , ) = 1
0.5 ( , ) × ( , ) = 0 ≠ , ( , , ) = 0 … … … eq. 6
1 ( ) × ( ) = −1, ( , , ) = −1
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4. of DF (whose values are less than 0.5, and converted them to a value 1, while all the remaining values change to
0, neglecting the value of the conflict and neutrality situations). In the same way, the creation of a binary matrix,
distance function of neutral (DFN), is accomplished from neutral values of DF (whose values are equal to 0.5,
and converted them to a value 1, while all the remaining values change to 0, neglecting the value of the conflict
and alliance situations).
Let DFC= (DFC)1
means the direct conflicts among the agents. Each element of (DFC)2
= (DFC)1 ×
(DFC)1 represents the number of conflict between two agents passing through a third agent. For example,
(DFC)2
[i][j]=3 means that there are three conflict passageways of length two between agent i and agent j. In the
same way (DFC)Agent#-1= (DFC)1 × (DFC)Agent#-2 can be obtained. DFA= (DFA)1
means the direct
alliances between any two agents. Each element of (DFA)2
=(DFA)1× (DFA)1 represents the number of
alliances between two agents passing through a third agent. For example, (DFA)2
[i][j]=3 means that there are
three alliance passageways of length two between agent i and agent j.
In the same way (DFA)Agent#-1 =(DFA)1×(DFA)Agent#-2 can be obtained. Algorithm (3.21) presents this
strategy, consider step 7 to step 14. Weights are given for each generated DF such that DF of highest power is
assigned a weight of 1. The second DF of highest power is assigned a weight of 2 and so on, this process is
illustrated in steps 16 to 21. Steps 22 to 30 select a neutral value related to a pair of agents, then sum the
corresponding values in CPMs(conflict path matrices), sum the corresponding values in APMs(alliance path
matrices), and according to their difference, the predicted value will be assigned. These steps will be repeated
for all neutral values.
An amazing by-product result of the prediction process is that the logical ORing of CPMs indicates that
there is a direct conflict or indirect conflict of length 1, 2, or N, (number of agents), consider eq.7
= ( (CPM ))
#
… . eq. 7
Same saying can be adopted for APMs, consider eq.8
= ( (
#
)) … . . eq. 8
Subsequently, for example, CPM2=CPM1 ORing CPM2 indicates the existence of indirect conflict between two
agents through another agent, and so on for higher power CPMs. Algorithm presented in figure (1.1)
accomplishes this duty by modifying steps 34 to 42 of. The mathematical operations are replaced by logical
operations and in this way the expensive way of finding powered matrices.
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5. Figure .1 Predicted conflict and alliances algorithm
00 Algorithm opinion change prediction algorithm
01 Input: Distance function matrix//
N Agents# // number of agents
02 Output: // Conflict path matrices-CPM1.. CPMn-1
03 CPM1 , CPM2, … , CPMn-1
04 //Alliance path matrices- APM1..APMn-1
05 APM1,APM2,..APMn-1
06 Prediction_matrix =IS;
07 {
08 construct a matrix of conflict values only and zero other values; CPM1
09 construct a matrix of alliance values only and zero other values; APM1
10 construct a matrix of neutral values only and zero other values; DFN
11 for (i=2; i<N;I++)
12 { indirect_alliance_or_conflict(CPM1, CPMi-1, CPMi);
13 indirect_alliance_or_conflict(APM1, APMi-1, APMi);
14 }
15 // assign weight for indirect conflict matrices and alliances matrices
16 int w=N;
17 for (i=1; i<N;I++)
18 { multiply(w,CPMi, CPMi);
19 multiply(w,APMi, APMi);
20 w--;
21 }
22 //find prediction matrix
23 for each pair of neutral agents in IS, A1 and A2
24 { predicted_conflict_value= CPM1 [A1][A2] +…+ CPMn-1 [A1][A2];
25 predicted_alliance_value= APM1 [A1][A2] +…+APMn-1 [A1][A2];
26 if (predicted_conflict_value> predicted_alliance_value)
27 predicted_matrix[A1][A2]=-1;
28 else if (predicted_conflict_value< predicted_alliance_value)
29 predicted_matrix[A1][A2]=1;
30 // else do nothing; It is already 0 i.e., neutral
31 }
32 } // of the algorithm
33 // to find indirect conflict or coalition
34 indirect_alliance_or_conflict(one, two, three);
35 { for(int i=0; i<n; i++)
36 for(int j=0; j<n; j++)
37 { buffer=0;
38 for(int k=0; k<n; k++)
39 buffer=buffer+one[i][k]*two[k][j];
40 three[i][j]=buffer;
41 }// of for j
42 } // of the indirect_alliance_or_conflict
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6. Figure.2 Existence of indirect alliances and conflicts.
For more elucidation, consider the following example, which is designed to explain most aspects presented
in this section. Consider the following information system presented in Table .1. The values of this table
represent opinions of agents about specific issues restricted to values of (1; agreement, 0; neutrality, -1;
disagreement). This information system contains five agents (1, 2, 3, 4, and 5) with four issues (a, b, c, and d).
The distance function DF between agents has been computed and the results are shown in Table .2. conflicts
and alliances.
After computing the distance function, its graph is obtained as presented in Figure.3 for more clarity.
Each node represents agent. The dotted line, which connects between any two nodes, represents the alliance
situation existing between two agents. The solid line represents the conflict situation.
Figure.3 a graphical representation of
distance between agents for all issues
As it is evident from the graph, there is no direct relation between agent# 4 and agent# 3, agent# 5 and
agent# 2, or between agent# 1 and agent# 2. According to the feature coalition relation in Pawlak conflict
theory :
( , ) ( , ) ( , ),
This condition can be translated as a phrase: "friend of my friend is my friend". Therfore it is possible
to expect of indirect relationships between neutral agents based on their relationships with other agents.
After applying algorithms in Figure.1 and Figure.2, from step 8 and step 9, the APM1 that denotes
DFA (Distance Function of Alliance), the CPM1 denotes DFC (Distance Function of Conflict) and finally
A/U a b c d
1 -1 0 1 0
2 0 1 0 -1
3 -1 0 1 -1
4 -1 1 0 1
5 1 1 -1 1
Table.1 Information System
agents 1 2 3 4 5
1 0 0 0 0 0
2 0.5 0 0 0 0
3 0.250 0.375 0 0 0
4 0.375 0.500 0.5 0 0
5 0.750 0.5 0.875 0.375 0
Table.2 Distance Function
……
01 // to find the matrix of indirect conflict or coalition existence
02 existence_of_indirect_alliance_or_conflict(one, two, three);
03 { for(int i=0; i<n; i++)
04 for(int j=0; j<n; j++)
05 { buffer=0;
06 for(int k=0; k<n; k++)
07 buffer=buffer ORING one[i][k] ANDING two[k][j];
08 three[i][j]=buffer;
09 }
10 }
…..
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7. DFN (Distance Function of Neutral) for all issues are presented in Table.3 , Table.4, and Table.5
respectively.
After that DFA2, DFA3, …., DFAn-1 have been calculated to extract indirect alliance passageway
between agents. Table.6, Table.7, and Table.8 with Figure.4, Figure.5, and Figure.6 represent indirect alliance
passageway with various numbers and lengths respectively.
TABLE.6 DFA2
:NUMBER OF INDIRECT ALLIANCE
PASSAGEWAYS OF LENGTH TWO BETWEEN AGENTS
Passageways between agent# 1&2
Passageways between agent# 3&4
Passageways between agent# 1&5 New Predicted pasageways
Figure.4 DFA2
:number of indirect alliance passageways of length two between agents
Table.3 distance function of
1 2 3 4 5
1 0 0 1 1 0
2 0 0 1 0 0
3 1 1 0 0 0
4 1 0 0 0 1
5 0 0 0 1 0
alliance DFA
Table.4 distance function of
conflict DFC
1 2 3 4 5
1 0 0 0 0 0
2 0 0 0 0 0
3 0 0 0 0 0
4 0 0 0 0 0
5 1 0 1 0 0
Table.5 distance function of
1 2 3 4 5
1 0 0 0 0 0
2 1 0 0 0 0
3 0 0 0 0 0
4 0 1 1 0 0
5 0 1 0 0 0
neutral DFN
agents 1 2 3 4 5
1 0 1 0 0 1
2 1 0 0 0 0
3 0 0 0 1 0
4 0 0 1 0 0
5 1 0 0 0 0
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8. agents 1 2 3 4 5
1 0 0 1 1 0
2 0 0 0 1 0
3 1 1 0 0 1
4 1 1 0 0 1
5 0 0 1 0 0
TABLE.7 DFA3
: NUMBER OF INDIRECT ALLIANCE
PASSAGEWAYS OF LENGTH THREE BETWEEN AGENTS
Passageways between agent# 1&3 Passageways between agent# 1&4
Passageways between agent# 2&3
Passageways between agent# 2&4
Passageways between agent# 3&5 Passageways between agent# 4&5
New Predicted pasagew
Figure.5 DFA3
: number of indirect alliance passageways of length three between agents
agents 1 2 3 4 5
1 0 2 0 0 2
2 1 0 0 0 1
3 0 0 0 2 0
4 0 0 2 0 0
5 1 1 0 0 0
Table.8 DFA4
: number of indirect alliance
passageways of length four between agents
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9. For example the value of DFA2 in position (1, 2), i.e., DFA2 [1] [2] =1, means that there is only one indirect
alliance passageway between agent#1 and agent#2. Actually, the claim is true, recall Figure.3 because there is
passageway through agent#3 as illustrated in Figure.7. In other word the neutrality relation between agent# 1
and agent# 2 can be changed to alliance relation because both agent# 1 and agent# 2 have alliance relation with
agent# 3.
In same way DFA3[2][4]=1, which mean that there is one passageway between agent#2 and agent#4 of length
three as shown in Figure.8.
Passageways between agent# 1&2 Passageways between agent# 1&2
Passageways between agent# 1&5
Passageways between agent# 1&5
Passageways between agent# 2&5 Passageways between agent# 3&4
Passageways between agent# 3&4
New Predicted pasagew
Figure.6 DFA4: number of indirect alliance passageways of length four between agents
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10. After finding all the indirect passageways between the neutral agents, these matrices are combined now
using eq.7, and converted to a single matrix representing all direct and indirect alliance passageways, regardless
of their length, then the resulted matrix, graph, will be converted to a binary matrix as shown in Table.9.
TABLE.9 ALL DIRECT AND INDIRECT ALLIANCE
PASSAGEWAYS
agents 1 2 3 4 5
1 0 0 0 0 0
2 1 0 0 0 0
3 1 1 0 0 0
4 1 1 1 0 0
5 1 1 1 1 0
Similarly, all previous operations are repeated to the conflict situation. All indirect alliance
passageways through conflict that may lead to alliance are calculated (Table.10, Table.11, Table.12) and then a
binary matrix of conflict is found as in Table.13.
TABLE.10 DFC2
:NUMBER OF INDIRECT ALLIANCE
PASSAGEWAYS THROUGH CONFLICT RELATION OF LENGTH
TWO BETWEEN AGENTS
agents 1 2 3 4 5
1 0 0 1 0 0
2 0 0 0 0 0
3 1 0 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
TABLE.11 DFC3: NUMBER OF INDIRECT ALLIANCE
PASSAGEWAYS THROUGH CONFLICT RELATION OF LENGTH
TWO BETWEEN AGENTS
agents 1 2 3 4 5
1 0 0 0 0 0
2 0 0 0 0 0
3 0 0 0 0 0
4 0 0 0 0 0
5 1 0 1 0 0
TABLE.12 DFC4: NUMBER OF INDIRECT ALLIANCE
PASSAGEWAYS THROUGH CONFLICT RELATION OF LENGTH
TWO BETWEEN AGENTS
agents 1 2 3 4 5
1 0 0 1 0 0
2 0 0 0 0 0
3 1 0 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
TABLE.13 ALL DIRECT AND INDIRECT ALLIANCE
PASSAGEWAYS
agents 1 2 3 4 5
1 0 0 1 0 1
2 0 0 0 0 0
3 1 0 0 0 1
4 0 0 0 0 0
5 1 0 1 0 0
Figure.7 indirect alliance passageway of length two between
agent#1& agent#2
Figure.8 indirect alliance passageway of length three between
agent#2& agent#4
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11. Finally, this module will compared the binary conflict matrix and the binary alliance matrix. It regards the
higher priority for the direct connection, i.e., when there is a direct conflict agent #n and agent #m in binary
conflict matrix and there is indirect connection between same agents in the binary alliance matrix, then the
priority for the direct conflict. Then farther operations used in this module are:
1. The XORING process was performed between the binary alliance matrix, Table.9, and the original
conflict matrix, Table.4, to obtain direct and new predicted alliance passageways. The result is shown
in Figure.9.
2. The ANDING process between the neutrality matrix in Table.5 was carried out with the binary alliance
matrix in Table.9, to show only a new predicted passageways that previously were neutral as shown in
Figure.10.
Figure 9 direct and new predicted alliance passageways Figure.10 New predicted passageways
Consequently, new alliances have been predicted in future relations between the agents as shown in
Figure.11, through applying of the famous saying "friend of my friend is my friend".
Figure.11 Existing and predicted relations
IV. Conclusions
This research presented unprecedented algorithm to develop an aspect of conflict theory in which there
is no considerable progress was attained. The following are some conclusions obtained from this research:
- The escalation in neutral opinions of agents in the information system leads to ambiguity in relations
among agents and lack of clarity of vision.
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12. - It is possible to benefit from having relationships of neutral agents with other agents, which have
alliance or conflict relations to discover indirect passageways, predict of future relationships between
neutral agents who have a limiting relationships, and to expect the tendencies of their opinions through
their limited relationship with other agents.
- The research produces a practical method to implement the extension of "Enmity" and "friendship"
concepts. However, there is no way to prove the credibility of the proposed modification except the
actual occurrence of the predicted changes of the model in the real life conflict problem, and this fact
matches the properties of the original conflict theory.
- An important by-product achievement of the proposed algorithm is that it can be used to find indirect
paths of different lengths in any graph or digraph.
- The proposed operations; graph ORing, ANDing, and XORing, can be used for different purposes
such as social networks.
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International Journal of Computer Science and Information Security (IJCSIS),
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ISSN 1947-5500