DOCSLIB.ORG

The Implementation of Intelligent Oos Networking by the Development

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Implementation of Intelligent Oos Networking by the Development Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2003 The Implementation of Intelligent QoS Networking by the Development and Utilization of Novel Cross-Disciplinary Soft Computing Theories and Techniques Ahmed Shawky Moussa Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES THE IMPLEMENTATION OF INTELLIGENT QoS NETWORKING BY THE DEVELOPMENT AND UTILIZATION OF NOVEL CROSS-DISCIPLINARY SOFT COMPUTING THEORIES AND TECHNIQUES By AHMED SHAWKY MOUSSA A Dissertation submitted to the Department of Computer Science In partial fulfillment of the requirements for The degree of Doctor of Philosophy Degree Awarded: Fall Semester, 2003 The members of the committee approve the dissertation of Ahmed Shawky Moussa Defended on September 19, 2003. ______________________ Ladislav Kohout Major Professor ______________________ Michael Kasha Outside Committee Member ______________________ Lois Wright Hawkes Committee Member ______________________ Ernest McDuffie Committee Member ______________________ Xin Yuan Committee Member The Office of Graduate Studies has verified and approved the above named committee members ii Optimization is not just a technique, or even a whole science; it is an attitude and a way of life. My mother’s life is a great example of optimization, making the best out of every thing, sometimes out of nothing. She has also been the greatest example of sacrifice, dedication, selfishness, and many other qualities, more than can be listed. I dedicate this work and all my past and future works to the strongest person I have ever known, the one who taught me, by example, what determination is, Fawkeya Ibrahim, my mother. iii ACKNOWLEDGMENTS The preparation for this work started with my birth. Every thing I learned since that day contributed to this work. I certainly have had many teachers, friends, and advisors who helped shaping my knowledge, skills, and attitudes. I value and thank them all, especially my mother who has given me all she has, her life, Mohamed Kamel, my mentor and spiritual father during my growing years, El-Raee Abdo Sheta, whose instructions played a great role in forming my attitudes. At Florida State University I have had tremendous support and help, without which this work would have never been possible. My thanks are due to the staff of the International Center, the Thagard Student Health Center, and the Center for Music Research, especially Dr. Jack Taylor. I am deeply thankful to all the faculty, staff, and colleagues who helped me in every way they could. I have taken an extraordinary number of classes in the Department of Computer Science and other departments too. These classes were building blocks in constructing my multidisciplinary scientific background. Certain classes have had special impact on this dissertation and were milestones in my progression: Soft Computing by Dr. Ladislav Kohout, Computational Science and Engineering, and Parallel Programming by Dr. Kyle Gallivan, Algorithm Analysis and Design by Dr. Stephen Leach, and the Seminar on QoS Networking by Dr. Xin Yuan. iv My gratitude goes to Dr. Ladislav Kohout, my major professor. I thank him for his faith in me and for guiding me on the right track toward a career in computer science research. I appreciate his open mindedness and vast knowledge, which he always made available for me. I certainly am lucky to have learned Computational Intelligence from one of the leading pioneers in the field. I am greatly indebted to all my committee members, who gave me their knowledge, support, and resources, even before they were assigned to my PhD committee. Special appreciation is due to Dr. Lois Wright Hawkes who has gone a long way beyond duty to support me in the difficult times, especially in the absence of my major professor, without receiving official credit or recognition. No words would ever be enough to express my gratitude and appreciation for Professor Michael Kasha. I feel extremely privileged and proud to have known, and learned from, such a world-renowned top-notch scientist. He has been a very generous source of knowledge and support, and a role model to follow. I appreciate his tremendous enthusiasm in teaching, coaching, and helping me, and I hope I can live up to his expectation in my scientific career. Finally, I would like to express my gratitude and love to my wife Shaheera, whose dedication, love, and assistance made this work possible. She has been the best partner in my journey to the PhD degree. v TABLE OF CONTENTS List of Tables ……………………………………………………………………. xii List of Figures ………………..………………………………………………… xiii Abstract ………………………………………………………………………..... xv INTRODUCTION ……………………………………………………………….. 1 1. Soft Computing ……………………………………….………………….…. 8 Fuzzy Computing ……………….…………………………….… 10 Fuzzy Sets …………………………………………….… 10 Fuzzy Relations …………………………………….…… 18 Fuzzy Logic …………………………….……………..… 21 Fuzzy Control …………………………….……………..… 26 Evolutionary Computing ……..…………….…………………… 27 Artificial Neural Networks …………………………………...… 29 Probabilistic Computing …………..……….…………………… 32 Bayesian Belief Networks ……………………………… 33 MTE/DST …………………………………………..…… 34 vi Fusion of Methodologies …..……..……….…………………..… 36 Probability Theory vs. Fuzzy Set Theory …………….… 36 Hybrid Soft Computing Algorithms ………………….… 37 Chapter Conclusion ………………..……….…………………..… 41 2. Quality of Service Distributed Computing ……………….…………… …… 43 Computer Networks ……………………………………………. 43 Types of Computer Networks …………………….….…. 44 Characteristics of Computer Networks …………………. 45 Interconnection Topology ………………………………. 46 Routing …………………………………………………. 50 Quality of Service Computer Networks …………………….… 51 QoS Performance Measures …………………………… 52 QoS Levels ……………………………..……………… 54 QoS Routing …………………………………………… 56 3. Preliminary Review of the Literature of the Fusion of Networking and Soft Computing …………………………………………………………….. …. 60 Intelligent Networking ……………………..………………….. 61 vii The Uncertainty in Network State Information ………………. 64 Adaptive Control ………………………………………………… 69 4. Notable Deficiency in Fuzzy Set Theory ……………….…………………… 73 Fuzzy Set Theory and the Observed Deficiency…………………. 74 Introduction …………………….………………….….…. 74 The Problem Observation ……………….………………. 75 The Problem Identification ………………………………. 77 The Problem Specification and Justification………………. 81 The Problem Solution and Proposed Model………………. 82 5. Soft Probability ……………….……………………………………………… 87 Introductory Background…………………………………………. 88 The Dual Interpretation…………………………………………. 91 Important Questions………………………………………….…. 93 Previous Attempts…………………………………………….…. 94 Problem Definition and Formulation ……………………….….. 96 The Motivation for a Solution ……………………………….…. 98 The Success Criteria ………………………………………….…. 99 viii The Proposed Model ………………………………………….…. 101 Counting and Frequency …………………………..….…. 101 Cumulative Probability …………………….….…………. 107 6. Using BK-Products of Fuzzy Relations in Quality of Service Adaptive Communications …………………………………………………………….. 113 Introduction …………………………………………..………… 114 Previous approaches …………..……………………………….. 116 The Proposed Approach ………………………………..………… 117 Justifications of Validity ………….……………………………. 121 Fuzzy Control ………………………………………………….. 124 Chapter Conclusion and Future Directions …………………....… 129 7. A New Network State Information Updating Policy Using Fuzzy Tolerance Classes and Fuzzified Precision …………………………………………………….. 130 Introduction …………………………………………..………… 131 Network State Updating Policies ………………………..……… 132 Periodic Policies ……………………..…………..….…. 132 Triggering Policies ……………………..…………...…. 133 ix Precision Fuzzification …………………………………..…… 135 A New Network State Update Policy …………………..……… 138 Chapter Conclusion and Future Directions …………………..… 142 8. Fuzzy Probabilistic QoS Routing for Uncertain Networks …………………. 144 Introduction …………………………………………..………… 145 Fuzzy Probabilistic Computing .……………………..………… 147 QoS Routing and Related Work ………………………..………… 149 The New Algorithm …….……………………………..………… 151 The System State ……………………..…………..….…. 151 The Goals of the Algorithm …………..…………..….…. 152 Probability Distribution Computation ……………..….…. 153 The Acceptable Probability Values ……………..….…. 155 Algorithm Refinement …………….……………..….…. 155 Path Computation ………………………….……..….…. 160 Optimal Path Selection …………….……………..….…. 160 Chapter Summary and Conclusion …………………………..… 162 Final Conclusion, Summary, and Future Plans …………………………………. 164 x Dissertation Summary and Conclusion …………..…..………… 164 Future Plans ………………………….…………..…..………… 167 REFERENCES ……………………………………………..…………………… 170 Biographical Sketch …………………………………………………………..…. 184 xi LIST OF TABLES TABLE (1 – 1): Properties of Crisp vs. Fuzzy Relations ……………….. 18 TABLE (1 – 2): Increasing complexity of Pearl’s belief propagation on Bayesian Networks ………………………………………………………………… 33 xii LIST OF FIGURES FIGURE (1): The General Conceptual Structure of The Dissertation …..… 6 FIGURE (2): The Dissertation Flow Chart …………………………….… 7 FIGURE (1 – 1): Examples of Crisp Sets ……………………………….… 11 FIGURE (1 – 2): Fuzzy Set Representation ………………………...…… 12 FIGURE (1 – 3): Graphical Representation of the Analytical Representation ………………………………………………………………………….…. 14 FIGURE (1 – 4): Example fuzzy addition and subtraction………………. 17 FIGURE (2 – 1): Full Connectivity ……………………………………… 46 FIGURE (2 – 2): Crossbar Network Topology …………………………. 47 FIGURE (2 – 3): Ring Topology ………………...……………………… 48 FIGURE (2 – 4): Hypercube Topology
Load more

Recommended publications
  • A Simple Logic for Comparisons and Vagueness
    THEODORE J. EVERETT A SIMPLE LOGIC FOR COMPARISONS AND VAGUENESS ABSTRACT. I provide an intuitive, semantic account of a new logic for comparisons (CL), in which atomic statements are assigned both a classical truth-value and a “how much” value or extension in the range [0, 1]. The truth-value of each comparison is determined by the extensions of its component sentences; the truth-value of each atomic depends on whether its extension matches a separate standard for its predicate; everything else is computed classically. CL is less radical than Casari’s comparative logics, in that it does not allow for the formation of comparative statements out of truth-functional molecules. I argue that CL provides a better analysis of comparisons and predicate vagueness than classical logic, fuzzy logic or supervaluation theory. CL provides a model for descriptions of the world in terms of comparisons only. The sorites paradox can be solved by the elimination of atomic sentences. In his recent book on vagueness, Timothy Williamson (1994) attacks accounts of vagueness arising from either fuzzy logic or supervaluation theory, both of which have well-known problems, stemming in part from their denial of classical bivalence. He then gives his own, epistemic ac- count of vagueness, according to which the vagueness of a predicate is fundamentally a matter of our not knowing whether or not it applies in every case. My main purpose in this paper is not to criticize William- son’s positive view, but to provide an alternative, non-epistemic account of predicate vagueness, based on a very simple logic for comparisons (CL), which preserves bivalence.
    [Show full text]
  • Does Informal Logic Have Anything to Learn from Fuzzy Logic?
    University of Windsor Scholarship at UWindsor OSSA Conference Archive OSSA 3 May 15th, 9:00 AM - May 17th, 5:00 PM Does informal logic have anything to learn from fuzzy logic? John Woods University of British Columbia Follow this and additional works at: https://scholar.uwindsor.ca/ossaarchive Part of the Philosophy Commons Woods, John, "Does informal logic have anything to learn from fuzzy logic?" (1999). OSSA Conference Archive. 62. https://scholar.uwindsor.ca/ossaarchive/OSSA3/papersandcommentaries/62 This Paper is brought to you for free and open access by the Conferences and Conference Proceedings at Scholarship at UWindsor. It has been accepted for inclusion in OSSA Conference Archive by an authorized conference organizer of Scholarship at UWindsor. For more information, please contact [email protected]. Title: Does informal logic have anything to learn from fuzzy logic? Author: John Woods Response to this paper by: Rolf George (c)2000 John Woods 1. Motivation Since antiquity, philosophers have been challenged by the apparent vagueness of everyday thought and experience. How, these philosophers have wanted to know, are the things we say and think compatible with logical laws such as Excluded Middle?1 A kindred attraction has been the question of how truth presents itself — relatively and in degrees? in approximations? in resemblances, or in bits and pieces? F.H. Bradley is a celebrated (or as the case may be, excoriated) champion of the degrees view of truth. There are, one may say, two main views of error, the absolute and relative. According to the former view there are perfect truths, and on the other side there are sheer errors..
    [Show full text]
  • Security Distributed and Networking Systems (507 Pages)
    Security in ................................ i Distri buted and /Networking.. .. Systems, . .. .... SERIES IN COMPUTER AND NETWORK SECURITY Series Editors: Yi Pan (Georgia State Univ., USA) and Yang Xiao (Univ. of Alabama, USA) Published: Vol. 1: Security in Distributed and Networking Systems eds. Xiao Yang et al. Forthcoming: Vol. 2: Trust and Security in Collaborative Computing by Zou Xukai et al. Steven - Security Distributed.pmd 2 5/25/2007, 1:58 PM Computer and Network Security Vol. 1 Security in i Distri buted and /Networking . Systems Editors Yang Xiao University of Alabama, USA Yi Pan Georgia State University, USA World Scientific N E W J E R S E Y • L O N D O N • S I N G A P O R E • B E I J I N G • S H A N G H A I • H O N G K O N G • TA I P E I • C H E N N A I Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. SECURITY IN DISTRIBUTED AND NETWORKING SYSTEMS Series in Computer and Network Security — Vol. 1 Copyright © 2007 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.
    [Show full text]
  • Distributed Computing Network System, Said to Be "Distributed" When the Computer Programming and the Data to Be Worked on Are Spread out Over More Than One Computer
    Praveen Balda et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.4, April- 2015, pg. 761-767 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 4, Issue. 4, April 2015, pg.761 – 767 RESEARCH ARTICLE Security Enhancement in Distributed Networking Praveen Balda, Sh. Matish Garg Distributed Networking is a distributed computing network system, said to be "distributed" when the computer programming and the data to be worked on are spread out over more than one computer. Usually, this is implemented over a network. Prior to the emergence of low-cost desktop computer power, computing was generally centralized to one computer. Although such centers still exist, distribution networking applications and data operate more efficiently over a mix of desktop workstations, local area network servers, regional servers, Web servers, and other servers. One popular trend is client/server computing. This is the principle that a client computer can provide certain capabilities for a user and request others from other computers that provide services for the clients. (The Web's Hypertext Transfer Protocol is an example of this idea.) Enterprises that have grown in scale over the years and those that are continuing to grow are finding it extremely challenging to manage their distributed network in the traditional client/server computing model. The recent developments in the field of cloud computing has opened up new possibilities. Cloud-based networking vendors have started to sprout offering solutions for enterprise distributed networking needs.
    [Show full text]
  • Intransitivity and Vagueness
    Intransitivity and Vagueness Joseph Y. Halpern∗ Cornell University Computer Science Department Ithaca, NY 14853 [email protected] http://www.cs.cornell.edu/home/halpern Abstract preferences. Moreover, it is this intuition that forms the ba- sis of the partitional model for knowledge used in game the- There are many examples in the literature that suggest that indistinguishability is intransitive, despite the fact that the in- ory (see, e.g., [Aumann 1976]) and in the distributed sys- distinguishability relation is typically taken to be an equiv- tems community [Fagin, Halpern, Moses, and Vardi 1995]. alence relation (and thus transitive). It is shown that if the On the other hand, besides the obvious experimental obser- uncertainty perception and the question of when an agent vations, there have been arguments going back to at least reports that two things are indistinguishable are both care- Poincare´ [1902] that the physical world is not transitive in fully modeled, the problems disappear, and indistinguishabil- this sense. In this paper, I try to reconcile our intuitions ity can indeed be taken to be an equivalence relation. More- about indistinguishability with the experimental observa- over, this model also suggests a logic of vagueness that seems tions, in a way that seems (at least to me) both intuitively to solve many of the problems related to vagueness discussed appealing and psychologically plausible. I then go on to ap- in the philosophical literature. In particular, it is shown here ply the ideas developed to the problem of vagueness. how the logic can handle the Sorites Paradox. To understand the vagueness problem, consider the well- n+1 1 Introduction known Sorites Paradox: If grains of sand make a heap, then so do n.
    [Show full text]
  • On the Relationship Between Fuzzy Autoepistemic Logic and Fuzzy Modal Logics of Belief
    JID:FSS AID:6759 /FLA [m3SC+; v1.204; Prn:31/03/2015; 9:42] P.1(1-26) Available online at www.sciencedirect.com ScienceDirect Fuzzy Sets and Systems ••• (••••) •••–••• www.elsevier.com/locate/fss On the relationship between fuzzy autoepistemic logic and fuzzy modal logics of belief ∗ Marjon Blondeel d, ,1, Tommaso Flaminio a,2, Steven Schockaert b, Lluís Godo c,3, Martine De Cock d,e a Università dell’Insubria, Department of Theorical and Applied Science, Via Mazzini 5, 21100 Varese, Italy b Cardiff University, School of Computer Science and Informatics, 5The Parade, Cardiff, CF24 3AA, UK c Artficial Intelligence Research Institute (IIIA) – CSIC, Campus UAB, 08193 Bellaterra, Spain d Ghent University, Department of Applied Mathematics, Computer Science and Statistics, Krijgslaan 281 (S9), 9000 Gent, Belgium e University of Washington Tacoma – Center for Web and Data Science, 1900 Commerce Street, Tacoma, WA 98402-3100, USA Received 12 February 2014; received in revised form 30 September 2014; accepted 26 February 2015 Abstract Autoepistemic logic is an important formalism for nonmonotonic reasoning originally intended to model an ideal rational agent reflecting upon his own beliefs. Fuzzy autoepistemic logic is a generalization of autoepistemic logic that allows to represent an agent’s rational beliefs on gradable propositions. It has recently been shown that, in the same way as autoepistemic logic generalizes answer set programming, fuzzy autoepistemic logic generalizes fuzzy answer set programming as well. Besides being related to answer set programming, autoepistemic logic is also closely related to several modal logics. To investigate whether a similar relationship holds in a fuzzy logical setting, we firstly generalize the main modal logics for belief to the setting of finitely-valued c Łukasiewicz logic with truth constants Łk, and secondly we relate them with fuzzy autoepistemic logics.
    [Show full text]
  • The Orthogonality Between Complex Fuzzy Sets and Its Application to Signal Detection
    Article The Orthogonality between Complex Fuzzy Sets and Its Application to Signal Detection Bo Hu 1 ID , Lvqing Bi 2 and Songsong Dai 3,* ID 1 School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China; [email protected] 2 School of Electronics and Communication Engineering, Yulin Normal University, Yulin 537000, China; [email protected] 3 School of Information Science and Engineering, Xiamen University, Xiamen 361005, China * Correspondence: [email protected]; Tel.: +86-592-258-0135 Academic Editor: Hsien-Chung Wu Received: 25 July 2017; Accepted: 25 August 2017; Published: 31 August 2017 Abstract: A complex fuzzy set is a set whose membership values are vectors in the unit circle in the complex plane. This paper establishes the orthogonality relation of complex fuzzy sets. Two complex fuzzy sets are said to be orthogonal if their membership vectors are perpendicular. We present the basic properties of orthogonality of complex fuzzy sets and various results on orthogonality with respect to complex fuzzy complement, complex fuzzy union, complex fuzzy intersection, and complex fuzzy inference methods. Finally, an example application of signal detection demonstrates the utility of the orthogonality of complex fuzzy sets. Keywords: orthogonality; complex fuzzy sets; complex fuzzy operations; complex fuzzy inference 1. Introduction Complex fuzzy sets [1] are an important extension of fuzzy set theory. In recent years, complex fuzzy sets have been successfully used in complex fuzzy inference systems for various applications, such as time series prediction [2–8], function approximation [9–11], and image restoration [12,13]. A complex fuzzy set A on a universe of discourse U is a mapping from U to the unit disc in the complex plane.
    [Show full text]
  • Consequence and Degrees of Truth in Many-Valued Logic
    Consequence and degrees of truth in many-valued logic Josep Maria Font Published as Chapter 6 (pp. 117–142) of: Peter Hájek on Mathematical Fuzzy Logic (F. Montagna, ed.) vol. 6 of Outstanding Contributions to Logic, Springer-Verlag, 2015 Abstract I argue that the definition of a logic by preservation of all degrees of truth is a better rendering of Bolzano’s idea of consequence as truth- preserving when “truth comes in degrees”, as is often said in many-valued contexts, than the usual scheme that preserves only one truth value. I review some results recently obtained in the investigation of this proposal by applying techniques of abstract algebraic logic in the framework of Łukasiewicz logics and in the broader framework of substructural logics, that is, logics defined by varieties of (commutative and integral) residuated lattices. I also review some scattered, early results, which have appeared since the 1970’s, and make some proposals for further research. Keywords: many-valued logic, mathematical fuzzy logic, truth val- ues, truth degrees, logics preserving degrees of truth, residuated lattices, substructural logics, abstract algebraic logic, Leibniz hierarchy, Frege hierarchy. Contents 6.1 Introduction .............................2 6.2 Some motivation and some history ...............3 6.3 The Łukasiewicz case ........................8 6.4 Widening the scope: fuzzy and substructural logics ....9 6.5 Abstract algebraic logic classification ............. 12 6.6 The Deduction Theorem ..................... 16 6.7 Axiomatizations ........................... 18 6.7.1 In the Gentzen style . 18 6.7.2 In the Hilbert style . 19 6.8 Conclusions .............................. 21 References .................................. 23 1 6.1 Introduction Let me begin by calling your attention to one of the main points made by Petr Hájek in the introductory, vindicating section of his influential book [34] (the italics are his): «Logic studies the notion(s) of consequence.
    [Show full text]
  • A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability and Statistics
    FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 1 Contents: Preface by C. Le: 3 0. Introduction: 9 1. Neutrosophy - a new branch of philosophy: 15 2. Neutrosophic Logic - a unifying field in logics: 90 3. Neutrosophic Set - a unifying field in sets: 125 4. Neutrosophic Probability - a generalization of classical and imprecise probabilities - and Neutrosophic Statistics: 129 5. Addenda: Definitions derived from Neutrosophics: 133 2 Preface to Neutrosophy and Neutrosophic Logic by C.
    [Show full text]
  • Zerohack Zer0pwn Youranonnews Yevgeniy Anikin Yes Men
    Zerohack Zer0Pwn YourAnonNews Yevgeniy Anikin Yes Men YamaTough Xtreme x-Leader xenu xen0nymous www.oem.com.mx www.nytimes.com/pages/world/asia/index.html www.informador.com.mx www.futuregov.asia www.cronica.com.mx www.asiapacificsecuritymagazine.com Worm Wolfy Withdrawal* WillyFoReal Wikileaks IRC 88.80.16.13/9999 IRC Channel WikiLeaks WiiSpellWhy whitekidney Wells Fargo weed WallRoad w0rmware Vulnerability Vladislav Khorokhorin Visa Inc. Virus Virgin Islands "Viewpointe Archive Services, LLC" Versability Verizon Venezuela Vegas Vatican City USB US Trust US Bankcorp Uruguay Uran0n unusedcrayon United Kingdom UnicormCr3w unfittoprint unelected.org UndisclosedAnon Ukraine UGNazi ua_musti_1905 U.S. Bankcorp TYLER Turkey trosec113 Trojan Horse Trojan Trivette TriCk Tribalzer0 Transnistria transaction Traitor traffic court Tradecraft Trade Secrets "Total System Services, Inc." Topiary Top Secret Tom Stracener TibitXimer Thumb Drive Thomson Reuters TheWikiBoat thepeoplescause the_infecti0n The Unknowns The UnderTaker The Syrian electronic army The Jokerhack Thailand ThaCosmo th3j35t3r testeux1 TEST Telecomix TehWongZ Teddy Bigglesworth TeaMp0isoN TeamHav0k Team Ghost Shell Team Digi7al tdl4 taxes TARP tango down Tampa Tammy Shapiro Taiwan Tabu T0x1c t0wN T.A.R.P. Syrian Electronic Army syndiv Symantec Corporation Switzerland Swingers Club SWIFT Sweden Swan SwaggSec Swagg Security "SunGard Data Systems, Inc." Stuxnet Stringer Streamroller Stole* Sterlok SteelAnne st0rm SQLi Spyware Spying Spydevilz Spy Camera Sposed Spook Spoofing Splendide
    [Show full text]
  • Fuzzy Relational Maps and Neutrosophic Relational Maps
    University of New Mexico UNM Digital Repository Faculty and Staff Publications Mathematics 2004 FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS Florentin Smarandache University of New Mexico, [email protected] W.B. Vasantha Kandasamy [email protected] Follow this and additional works at: https://digitalrepository.unm.edu/math_fsp Part of the Algebraic Geometry Commons, Analysis Commons, and the Set Theory Commons Recommended Citation W.B. Vasantha Kandasamy & F. Smarandache. FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS. Church Rock: Hexis, 2004. This Book is brought to you for free and open access by the Mathematics at UNM Digital Repository. It has been accepted for inclusion in Faculty and Staff Publications by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected], [email protected], [email protected]. W. B. VASANTHA KANDASAMY FLORENTIN SMARANDACHE FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS HEXIS Church Rock 2004 FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS W. B. Vasantha Kandasamy Department of Mathematics Indian Institute of Technology, Madras Chennai – 600036, India e-mail: [email protected] web: http://mat.iitm.ac.in/~wbv Florentin Smarandache Department of Mathematics University of New Mexico Gallup, NM 87301, USA e-mail: [email protected] HEXIS Church Rock 2004 1 This book can be ordered in a paper bound reprint from: Books on Demand ProQuest Information & Learning (University of Microfilm International) 300 N. Zeeb Road P.O. Box 1346, Ann Arbor MI 48106-1346, USA Tel.: 1-800-521-0600 (Customer Service) http://wwwlib.umi.com/bod/ and online from: Publishing Online, Co.
    [Show full text]
  • Presentazione Di Powerpoint
    Fog Computing, its Applications in Industrial IoT, and its Implications for the Future of 5G Flavio Bonomi, CEO and Co-Founder, Nebbiolo Technologies IEEE 5G Summit, Honolulu, May 5th, 2017 Agenda • Fog Computing and 5G: High Level Introduction • Architectural Angles in “Fog” with Relevance to 5G • Fog Computing and 5G: Natural Partners for the Future of Industrial IoT, with Applications • Nebbiolo Technologies: Brief Introduction • Conclusions © Nebbiolo Technologies The Pendulum Swinging Back: A Renewed Focus on the Edge of the Network, Motivated by the Network Evolution, 5G and IoT Fog Computing Mobile Edge Computing (Modern, Real-Time Capable) Edge Computing Real-Time Edge Cloud © Nebbiolo Technologies 3 The Internet of Things: Information Technologies “Meet” Operational Technologies Public Clouds Private Clouds Information Technologies Today: 1. Clouds 2. Enterprise Datacenters 3. Traditional and Embedded Public and Private Endpoints Communications 4. Networking Infrastructure (Internet, Mobile, Enterprise Ethernet, WiFi) Enterprise Data Center The Internet of Things Brings Together Information Domain and Operations Domain through: Traditional ICT end-points Information Technologies 1. Connectivity 2. Data Sharing and Analysis 3. Technology Convergence Operational Technologies Machines, devices, sensors, actuators, things © Nebbiolo Technologies 4 The Future 5G Network and Industrial IoT Both Require More Distributed Computing Public Clouds Private Clouds The Future 5G networks and IoT require more virtualized, scalable, reliable,
    [Show full text]