Logical Entropy: Introduction to Classical and Quantum Logical Information Theory
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Specification of Agent Explicit Knowledge in Cryptographic
International Journal of Electrical and Computer Engineering 4:2 2009 Specification of Agent Explicit Knowledge in Cryptographic Protocols Khair Eddin Sabri, Ridha Khedri, and Jason Jaskolka Department of Computing and Software Faculty of Engineering McMaster University {sabrike, khedri, jaskolj}@mcmaster.ca Abstract— Cryptographic protocols are widely used in various The procedural knowledge involves a set of mecha- applications to provide secure communications. They are usually nisms/functions that enables an agent to obtain new informa- represented as communicating agents that send and receive messages. tion from its explicit knowledge. For example, if the explicit These agents use their knowledge to exchange information and communicate with other agents involved in the protocol. An agent knowledge of an agent contains an encrypted message as well knowledge can be partitioned into explicit knowledge and procedural as the key and the cipher used to decrypt the message, then by knowledge. The explicit knowledge refers to the set of information using the procedural knowledge, the secret can be obtained. which is either proper to the agent or directly obtained from other In this paper, we focus only on the explicit knowledge. agents through communication. The procedural knowledge relates to Both parts of the agent knowledge are necessary in ana- the set of mechanisms used to get new information from what is already available to the agent. lyzing cryptographic protocols. When we analyze the liter- In this paper, we propose a mathematical framework which spec- ature using this classification of agent knowledge, we find ifies the explicit knowledge of an agent involved in a cryptographic several representations of that knowledge. -
Non-Locality, Contextuality and Valuation Algebras: a General Theory of Disagreement Samson Abramsky1, Giovanni Carù1
Non-locality, contextuality and valuation algebras: a general theory of disagreement Samson Abramsky1, Giovanni Carù1 1Department of Computer Science, University of Subject Areas: Oxford, Wolfson Building, Parks Road, Oxford OX1 Quantum information, quantum 3QD, U.K. foundations, generic inference Keywords: We establish a strong link between two apparently Contextuality, generic inference, unrelated topics: the study of conflicting information valuation algebras, algorithms in the formal framework of valuation algebras, and the phenomena of non-locality and contextuality. In particular, we show that these peculiar features of Email addresses: quantum theory are mathematically equivalent to a Samson Abramsky general notion of disagreement between information [email protected] sources. This result vastly generalises previously observed connections between contextuality, relational databases, constraint satisfaction problems, and logical paradoxes, and gives further proof that contextual behaviour is not a phenomenon limited to quantum physics, but pervades various domains of mathematics and computer science. The connection allows to translate theorems, methods and algorithms from one field to the other, and paves the way for the application of generic inference algorithms to study contextuality. 1. Introduction Non-locality and contextuality are characteristic features of quantum theory which have been recently proven to play a crucial role as fundamental resources for quantum information and computation [1,2]. In 2011, Abramsky and Brandenburger introduced an abstract mathematical framework based on sheaf theory to describe these phenomena in a unified treatment, thereby providing a common general theory for the study of non-locality and contextuality, which had been carried out in a rather arXiv:1911.03521v1 [quant-ph] 8 Nov 2019 concrete, example-driven fashion until then [3]. -
On Homomorphism of Valuation Algebras∗
Communications in Mathematical Research 27(1)(2011), 181–192 On Homomorphism of Valuation Algebras∗ Guan Xue-chong1,2 and Li Yong-ming3 (1. College of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710062) (2. School of Mathematical Science, Xuzhou Normal University, Xuzhou, Jiangsu, 221116) (3. College of Computer Science, Shaanxi Normal University, Xi’an, 710062) Communicated by Du Xian-kun Abstract: In this paper, firstly, a necessary condition and a sufficient condition for an isomorphism between two semiring-induced valuation algebras to exist are presented respectively. Then a general valuation homomorphism based on different domains is defined, and the corresponding homomorphism theorem of valuation algebra is proved. Key words: homomorphism, valuation algebra, semiring 2000 MR subject classification: 16Y60, 94A15 Document code: A Article ID: 1674-5647(2011)01-0181-12 1 Introduction Valuation algebra is an abstract formalization from artificial intelligence including constraint systems (see [1]), Dempster-Shafer belief functions (see [2]), database theory, logic, and etc. There are three operations including labeling, combination and marginalization in a valua- tion algebra. With these operations on valuations, the system could combine information, and get information on a designated set of variables by marginalization. With further re- search and theoretical development, a new type of valuation algebra named domain-free valuation algebra is also put forward. As valuation algebra is an algebraic structure, the concept of homomorphism between valuation algebras, which is derived from the classic study of universal algebra, has been defined naturally. Meanwhile, recent studies in [3], [4] have showed that valuation alge- bras induced by semirings play a very important role in applications. -
ONTOLOGICAL ENGINEERING for PUBLIC and PRIVATE PROCUREMENT Ontological Engineering for Public and Private Procurement
EUROPEAN UNION BRAZIL ONTOLOGICAL ENGINEERING FOR PUBLIC AND PRIVATE PROCUREMENT Ontological Engineering for Public and Private Procurement ONTOLOGICAL ENGINEERING FOR PUBLIC AND PRIVATE PROCUREMENT Ontological Engineering for Public and Private Procurement MINISTRY OF GOVERNMENT SECRETARIAT MINISTER OF STATE GEDDEL VIEIRA LIMA SECRETARY OF MICRO AND SMALL ENTERPRISES JOSÉ RICARDO DA VEIGA DIRECTOR OF MARKETS AND INNOVATION ALEXANDRE MONTEIRO E SILVA TECHNICAL SUPPORT TEAM FABIO MEDEIROS DE SOUZA (GENERAL-COORDINATOR OF MARKETS ACCESS) CARLOS VELOSO DE MELO JR. (FOREIGN TRADE ANALYST) JANIO MOREIRA DA COSTA (INFORMATION TECHNOLOGY ANALYST) EUROPEAN UNION DELEGATION TO BRAZIL HEAD OF THE EUROPEAN UNION DELEGATION JOÃO GOMES CRAVINHO MINISTER COUNSELLOR - HEAD OF DEVELOPMENT AND COOPERATION SECTION THIERRY DUDERMEL COOPERATION ATTACHÉ – EU-BRAZIL SECTOR DIALOGUES SUPPORT FACILITY COORDINATOR ONTOLOGICAL ASIER SANTILLAN LUZURIAGA IMPLEMENTING CONSORTIUM CESO DEVELOPMENT CONSULTANTS/FIIAPP/INA/CEPS ENGINEERING FOR MINISTRY OF PLANNING, DEVELOPMENT AND MANAGEMENT PUBLIC AND PRIVATE MINISTER OF STATE DYOGO OLIVEIRA SECRETARY OF MANAGEMENT PROCUREMENT GLEISSON CARDOSO RUBIN PROJECT NATIONAL DIRECTOR MARCELO MENDES BARBOSA CONTACTS PROJECT COORDINATION UNIT Authors: EUROPEAN UNION-BRAZIL SECTOR DIALOGUES SUPPORT FACILITY SECRETARIAT OF PUBLIC MANAGEMENT Edilson Ferneda MINISTRY OF PLANNING, DEVELOPMENT AND MANAGEMENT [email protected] TELEPHONE: + 55 61 2020.4645/4168/4785 Fernando William Cruz [email protected] [email protected] WWW.SECTORDIALOGUES.ORG © 2016 EUROPEAN UNION Brasília • March • 2016 THE CONTENT OF THIS PUBLICATION DOES NOT REFLECT THE OFFICIAL OPINION OF THE EUROPEAN UNION AND THE BRAZILIAN GOVERNMENT. RESPONSIBILITY FOR THE INFORMATION AND VIEWS EXPRESSED THEREIN LIES ENTIRELY WITH THE AUTHOR. Ontological Engineering for Public and Private Procurement LIST OF FIGURES AND CHARTS Figure 1.1 Some of the main knowledge organization systems ....................... -
On Measures of Entropy and Information
On Measures of Entropy and Information Tech. Note 009 v0.7 http://threeplusone.com/info Gavin E. Crooks 2018-09-22 Contents 5 Csiszar´ f-divergences 12 Csiszar´ f-divergence ................ 12 0 Notes on notation and nomenclature 2 Dual f-divergence .................. 12 Symmetric f-divergences .............. 12 1 Entropy 3 K-divergence ..................... 12 Entropy ........................ 3 Fidelity ........................ 12 Joint entropy ..................... 3 Marginal entropy .................. 3 Hellinger discrimination .............. 12 Conditional entropy ................. 3 Pearson divergence ................. 14 Neyman divergence ................. 14 2 Mutual information 3 LeCam discrimination ............... 14 Mutual information ................. 3 Skewed K-divergence ................ 14 Multivariate mutual information ......... 4 Alpha-Jensen-Shannon-entropy .......... 14 Interaction information ............... 5 Conditional mutual information ......... 5 6 Chernoff divergence 14 Binding information ................ 6 Chernoff divergence ................. 14 Residual entropy .................. 6 Chernoff coefficient ................. 14 Total correlation ................... 6 Renyi´ divergence .................. 15 Lautum information ................ 6 Alpha-divergence .................. 15 Uncertainty coefficient ............... 7 Cressie-Read divergence .............. 15 Tsallis divergence .................. 15 3 Relative entropy 7 Sharma-Mittal divergence ............. 15 Relative entropy ................... 7 Cross entropy -
Logic and the Development of Programming Languages, 1930–1975
LOGIC AND THE DEVELOPMENT OF PROGRAMMING LANGUAGES, 1930–1975 Peter Mark Priestley University College London Thesis submitted in fulfillment of requirements for the degree of PhD. May 2008 2 I, Peter Mark Priestley, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Abstract Compared with the history of computing hardware, the history of software is in a relatively unde- veloped state. In particular, the history of programming languages still consists for the most part of technical accounts presenting a rather Whiggish perspective on developments. Given the importance of software in the contemporary world, however, it is important to develop a more sophisticated un- derstanding of the medium in which it is expressed. This thesis considers some aspects of this history with the aim of examining the influence of formal logic on the evolution of notations for expressing computer programs. It is argued that this was not a natural or inevitable application of theory to practice, as is sometimes suggested, but a complex and contingent process with a rich history of its own. Two introductory chapters discuss the work on computability carried out by logicians in the mid-1930s, and the controversial topic of the role of logic in the invention of the computer. The body of the thesis proceeds chronologically, considering machine codes, the introduction of higher level notations, structured programming and software engineering, and the early object-oriented languages. The picture that emerges is that formal logic was deliberately employed by programming lan- guage designers to provide a model for a theoretical understanding of programming languages and the process of program development. -
Information Theory 1 Entropy 2 Mutual Information
CS769 Spring 2010 Advanced Natural Language Processing Information Theory Lecturer: Xiaojin Zhu [email protected] In this lecture we will learn about entropy, mutual information, KL-divergence, etc., which are useful concepts for information processing systems. 1 Entropy Entropy of a discrete distribution p(x) over the event space X is X H(p) = − p(x) log p(x). (1) x∈X When the log has base 2, entropy has unit bits. Properties: H(p) ≥ 0, with equality only if p is deterministic (use the fact 0 log 0 = 0). Entropy is the average number of 0/1 questions needed to describe an outcome from p(x) (the Twenty Questions game). Entropy is a concave function of p. 1 1 1 1 7 For example, let X = {x1, x2, x3, x4} and p(x1) = 2 , p(x2) = 4 , p(x3) = 8 , p(x4) = 8 . H(p) = 4 bits. This definition naturally extends to joint distributions. Assuming (x, y) ∼ p(x, y), X X H(p) = − p(x, y) log p(x, y). (2) x∈X y∈Y We sometimes write H(X) instead of H(p) with the understanding that p is the underlying distribution. The conditional entropy H(Y |X) is the amount of information needed to determine Y , if the other party knows X. X X X H(Y |X) = p(x)H(Y |X = x) = − p(x, y) log p(y|x). (3) x∈X x∈X y∈Y From above, we can derive the chain rule for entropy: H(X1:n) = H(X1) + H(X2|X1) + .. -
Information Theory and Maximum Entropy 8.1 Fundamentals of Information Theory
NEU 560: Statistical Modeling and Analysis of Neural Data Spring 2018 Lecture 8: Information Theory and Maximum Entropy Lecturer: Mike Morais Scribes: 8.1 Fundamentals of Information theory Information theory started with Claude Shannon's A mathematical theory of communication. The first building block was entropy, which he sought as a functional H(·) of probability densities with two desired properties: 1. Decreasing in P (X), such that if P (X1) < P (X2), then h(P (X1)) > h(P (X2)). 2. Independent variables add, such that if X and Y are independent, then H(P (X; Y )) = H(P (X)) + H(P (Y )). These are only satisfied for − log(·). Think of it as a \surprise" function. Definition 8.1 (Entropy) The entropy of a random variable is the amount of information needed to fully describe it; alternate interpretations: average number of yes/no questions needed to identify X, how uncertain you are about X? X H(X) = − P (X) log P (X) = −EX [log P (X)] (8.1) X Average information, surprise, or uncertainty are all somewhat parsimonious plain English analogies for entropy. There are a few ways to measure entropy for multiple variables; we'll use two, X and Y . Definition 8.2 (Conditional entropy) The conditional entropy of a random variable is the entropy of one random variable conditioned on knowledge of another random variable, on average. Alternative interpretations: the average number of yes/no questions needed to identify X given knowledge of Y , on average; or How uncertain you are about X if you know Y , on average? X X h X i H(X j Y ) = P (Y )[H(P (X j Y ))] = P (Y ) − P (X j Y ) log P (X j Y ) Y Y X X = = − P (X; Y ) log P (X j Y ) X;Y = −EX;Y [log P (X j Y )] (8.2) Definition 8.3 (Joint entropy) X H(X; Y ) = − P (X; Y ) log P (X; Y ) = −EX;Y [log P (X; Y )] (8.3) X;Y 8-1 8-2 Lecture 8: Information Theory and Maximum Entropy • Bayes' rule for entropy H(X1 j X2) = H(X2 j X1) + H(X1) − H(X2) (8.4) • Chain rule of entropies n X H(Xn;Xn−1; :::X1) = H(Xn j Xn−1; :::X1) (8.5) i=1 It can be useful to think about these interrelated concepts with a so-called information diagram. -
The Mathematical Theory of Information
Hecke, Oswald TeichmOller, Ernst Witt, are used for quantitative problems, Richard Courant, Edmund Landau, Fe- The Mathematical while hits (the number of correct clas- lix Hausdorff, Ernst Peschl, Paul Riebe- sifications), reliability, and nuts (von sell, Helmut Ulm, Alfred Stohr, Ernst Neumann's utility) appear in more qual- Zermelo, Gerhard Gentzen, Hans Pe- Theory of itative analyses. tersson, Erich K~ihler, and Wilhelm SOss. Although the author's ambition is to The names on this list range from the Information develop a (if not the) "mathematical the- committed Nazis, through non-Nazi by Jan Kahre ory of information," the embodiment is right wing nationalists, to naive, other- pre-mathematical. It is neither a naive worldly men who seemingly didn't BOSTON, KLUWER. 2002. 520 PP., US$50.O0 mathematical theory (as in naive set the- ISBN: 1-4020-7064-0 know much of what was going on, to ory) nor is it abused mathematics (in the those who stumbled into situations that REVIEWED BY CRISTIAN S. CALUDE sense of mathematics applied in mean- were beyond their control. The stories ingless ways). However, the mathemat- are gripping. In assessing guilt the ndoubtedly, the title of the book ical formalism is too rudimentary for a reader is hard pressed to decide with was well chosen: it is provoca- theory; I illustrate this point with two any certainty which people fall into U tive, promising, and full of infor- examples, the definitions of probability which categories. Teichm/_iller comes mation. Syntactically, the title can be and algorithmic complexity. The proba- off very badly, for example. -
Continuity in Information Algebras 3
August 28, 2018 22:6 Continuity CONTINUITY IN INFORMATION ALGEBRAS: A SURVEY ON THE RELATIONSHIP BETWEEN TWO TYPES OF INFORMATION ALGEBRAS XUECHONG GUAN∗ College of Mathematics and Information Science Shaanxi Normal University Xi’an, 710062, China [email protected] YONGMING LI College of Computer Science Shaanxi Normal University Xi’an, 710062, China [email protected] Received 24 June 2010 Revised (revised date) In this paper, the continuity and strong continuity in domain-free information algebras and labeled information algebras are introduced respectively. A more general concept of continuous function which is defined between two domain-free continuous information algebras is presented. It is shown that, with the operations combination and focusing, the set of all continuous functions between two domain-free s-continuous information algebras forms a new s-continuous information algebra. By studying the relationship between domain-free information algebras and labeled information algebras, it is demonstrated arXiv:1201.0414v1 [cs.AI] 2 Jan 2012 that they do correspond to each other on s-compactness. Keywords: domain-free continuous information algebra; labeled continuous information algebra; continuous function; compactness. 1. Introduction Inference under uncertainty is a common problem in the real world. Thus, for pieces of information from different sources, there always exist two fundamental aspects that to combine information and to exact information on a designated domain. Based on the above consideration, the valuation-based system (VBS) was first in- troduced by Shenoy. 1 Kohlas, in Ref. 2, has exactly introduced the concept of information algebra. We can see that information algebra is an algebraic structure links up with local computation and inference for treating uncertainty or, more ∗Work Address: College of Mathematic Science, Xuzhou Normal University, Xuzhou, 221116, China. -
A Characterization of Guesswork on Swiftly Tilting Curves Ahmad Beirami, Robert Calderbank, Mark Christiansen, Ken Duffy, and Muriel Medard´
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by MURAL - Maynooth University Research Archive Library 1 A Characterization of Guesswork on Swiftly Tilting Curves Ahmad Beirami, Robert Calderbank, Mark Christiansen, Ken Duffy, and Muriel Medard´ Abstract Given a collection of strings, each with an associated probability of occurrence, the guesswork of each of them is their position in a list ordered from most likely to least likely, breaking ties arbitrarily. Guesswork is central to several applications in information theory: Average guesswork provides a lower bound on the expected computational cost of a sequential decoder to decode successfully the transmitted message; the complementary cumulative distribution function of guesswork gives the error probability in list decoding; the logarithm of guesswork is the number of bits needed in optimal lossless one-to-one source coding; and guesswork is the number of trials required of an adversary to breach a password protected system in a brute-force attack. In this paper, we consider memoryless string-sources that generate strings consisting of i.i.d. characters drawn from a finite alphabet, and characterize their corresponding guesswork. Our main tool is the tilt operation on a memoryless string-source. We show that the tilt operation on a memoryless string-source parametrizes an exponential family of memoryless string-sources, which we refer to as the tilted family of the string-source. We provide an operational meaning to the tilted families by proving that two memoryless string-sources result in the same guesswork on all strings of all lengths if and only if their respective categorical distributions belong to the same tilted family. -
On the Structural Link Between Ontologies and Organised Data Sets on the STRUCTURAL LINK BETWEEN ONTOLOGIES AND
On the Structural Link Between Ontologies and Organised Data Sets ON THE STRUCTURAL LINK BETWEEN ONTOLOGIES AND ORGANISED DATA SETS BY ALICIA MARINACHE1, B.Eng. a thesis submitted to the department of computing and software and the school of graduate studies of mcmaster university in partial fulfilment of the requirements for the degree of Master of Applied Science c Copyright by Alicia Marinache2, February 2016 All Rights Reserved Master of Applied Science (2016) McMaster University (Software Engineering) Hamilton, Ontario, Canada TITLE: On the Structural Link Between Ontologies and Organ- ised Data Sets AUTHOR: Alicia Marinache3 B.Eng. (Software Engineering) University Politechnica, Bucharest, Romania SUPERVISOR: Dr. Ridha Khedri NUMBER OF PAGES: x, 132 ii To my family To my late father, who would have loved to see me pursuing my dreams Abstract "Relationships are complicated, sometimes interesting and can often appear unpredictable. This is just the type of situation that mathematics can help you with." - John D. Barrow, 100 essential things you didn't know you didn't know The proposed work focuses on articulating a mathematical framework to capture the structure of an ontology and relate it to organised data sets. In the discussed frame- work, the ontology structure captures the mereological relationships between con- cepts. It also uses other relationships relevant to the considered domain of application. The organized dataset component of the framework is represented using diagonal-free cylindric algebra. The proposed framework, called the domain-information structure, enables us to link concepts to data sets through a number of typed data operators. The new framework enhances concurrent reasoning on data for knowledge generation, which is essential for handling big data.