Formal Specification
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A Conceptual Framework for Investigating Organizational Control and Resistance in Crowd-Based Platforms
Proceedings of the 50th Hawaii International Conference on System Sciences | 2017 A Conceptual Framework for Investigating Organizational Control and Resistance in Crowd-Based Platforms David A. Askay California Polytechnic State University [email protected] Abstract Crowd-based platforms coordinate action through This paper presents a research agenda for crowd decomposing tasks and encouraging individuals to behavior research by drawing from the participate by providing intrinsic (e.g., fun, organizational control literature. It addresses the enjoyment) and/or extrinsic (e.g., status, money, need for research into the organizational and social social interaction, etc.) motivators [3]. Three recent structures that guide user behavior and contributions Information Systems (IS) reviews of crowdsourcing in crowd-based platforms. Crowd behavior is research [6, 4, 7] emphasize the importance of situated within a conceptual framework of designing effective incentive systems. However, organizational control. This framework helps research on motivation is somewhat disparate, with scholars more fully articulate the full range of various categorizations and often inconsistent control mechanisms operating in crowd-based findings as to which incentives are the most effective platforms, contextualizes these mechanisms into the [4]. Moreover, this approach can be limited by its context of crowd-based platforms, challenges existing often deterministic and rational assumptions of user rational assumptions about incentive systems, and behavior and motivation, which overlooks normative clarifies theoretical constructs of organizational and social aspects of human behavior [8]. To more control to foster stronger integration between fully understand the dynamics of crowd behavior and information systems research and organizational and governance of crowd-based projects, IS researchers management science. -
Data Types & Arithmetic Expressions
Data Types & Arithmetic Expressions 1. Objective .............................................................. 2 2. Data Types ........................................................... 2 3. Integers ................................................................ 3 4. Real numbers ....................................................... 3 5. Characters and strings .......................................... 5 6. Basic Data Types Sizes: In Class ......................... 7 7. Constant Variables ............................................... 9 8. Questions/Practice ............................................. 11 9. Input Statement .................................................. 12 10. Arithmetic Expressions .................................... 16 11. Questions/Practice ........................................... 21 12. Shorthand operators ......................................... 22 13. Questions/Practice ........................................... 26 14. Type conversions ............................................. 28 Abdelghani Bellaachia, CSCI 1121 Page: 1 1. Objective To be able to list, describe, and use the C basic data types. To be able to create and use variables and constants. To be able to use simple input and output statements. Learn about type conversion. 2. Data Types A type defines by the following: o A set of values o A set of operations C offers three basic data types: o Integers defined with the keyword int o Characters defined with the keyword char o Real or floating point numbers defined with the keywords -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
Parsing Arithmetic Expressions Outline
Parsing Arithmetic Expressions https://courses.missouristate.edu/anthonyclark/333/ Outline Topics and Learning Objectives • Learn about parsing arithmetic expressions • Learn how to handle associativity with a grammar • Learn how to handle precedence with a grammar Assessments • ANTLR grammar for math Parsing Expressions There are a variety of special purpose algorithms to make this task more efficient: • The shunting yard algorithm https://eli.thegreenplace.net/2010/01/02 • Precedence climbing /top-down-operator-precedence-parsing • Pratt parsing For this class we are just going to use recursive descent • Simpler • Same as the rest of our parser Grammar for Expressions Needs to account for operator associativity • Also known as fixity • Determines how you apply operators of the same precedence • Operators can be left-associative or right-associative Needs to account for operator precedence • Precedence is a concept that you know from mathematics • Think PEMDAS • Apply higher precedence operators first Associativity By convention 7 + 3 + 1 is equivalent to (7 + 3) + 1, 7 - 3 - 1 is equivalent to (7 - 3) – 1, and 12 / 3 * 4 is equivalent to (12 / 3) * 4 • If we treated 7 - 3 - 1 as 7 - (3 - 1) the result would be 5 instead of the 3. • Another way to state this convention is associativity Associativity Addition, subtraction, multiplication, and division are left-associative - What does this mean? You have: 1 - 2 - 3 - 3 • operators (+, -, *, /, etc.) and • operands (numbers, ids, etc.) 1 2 • Left-associativity: if an operand has operators -
A Classification and Comparison Framework for Software Architecture Description Languages
A Classification and Comparison Framework for Software Architecture Description Languages Neno Medvidovic Technical Report UCI-ICS-97-02 Department of Information and Computer Science University of California, Irvine Irvine, California 92697-3425 [email protected] February 1996 Abstract Software architectures shift the focus of developers from lines-of-code to coarser- grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is also used to classify and compare several existing ADLs. One conclusion is that, although much research has been done in this area, no single ADL fulfills all of the identified needs. I. Introduction Software architecture research is directed at reducing costs of developing applications and increasing the potential for commonality between different members of a closely related product family [GS93, PW92]. Software development based on common architectural idioms has its focus shifted from lines-of-code to coarser-grained architectural elements (software components and connectors) and their overall interconnection structure. -
The Holy Spirit and the Physical Uníverse: the Impact of Scjentific Paradigm Shifts on Contemporary Pneumatology
Theological Studies 70 (2009) . THE HOLY SPIRIT AND THE PHYSICAL UNÍVERSE: THE IMPACT OF SCJENTIFIC PARADIGM SHIFTS ON CONTEMPORARY PNEUMATOLOGY WOLFGANG VONDEY A methodological shift occurred in the sciences in the 20th century that has irreversible repercussions for a contemporary theology of the Holy Spirit. Newton and Einstein followed fundamentally different trajectories that provide radically dissimilar frame- works for the pneumatological endeavor. Pneumatology after Einstein is located in a different cosmological framework constituted by the notions of order, rationality, relationality, symmetry, and movement. These notions provide the immediate challenges to a contemporary understanding of the Spirit in the physical universe. HPHE PARADIGM SHIFT IN SCIENCE from Ptolemaic to Copernican cosmo- Â logy is clearly reflected in post-Enlightenment theology. The wide- ranging implications of placing the sun instead of the earth at the center of the universe marked the beginnings of both the scientific and religious revolutions of the 16th century. A century later, Isaac Newton provided for the first time a comprehensive system of physical causality that heralded space and time as the absolute constituents of experiential reality from the perspective of both natural philosophy and theology.^ Despite the echoes WOLFGANG VONDEY received his Ph.D. in systematic theology and ethics at Marquette University and is currently associate professor of systematic theology in the School of Divinity, Regent University, Virginia. A prolific writer on Pneu- matology, ecclesiology, and the dialogue of science and theology, he has most recently published: People of Bread: Rediscovering Ecclesiology (2008); "Pentecos- tal Perspectives on The Nature and Mission of the Church" in "The Nature and Mission of the Church": Ecclesial Reality and Ecumenical Horizons for the Twenty- First Century, ed. -
Arithmetic Expression Construction
1 Arithmetic Expression Construction 2 Leo Alcock Sualeh Asif Harvard University, Cambridge, MA, USA MIT, Cambridge, MA, USA 3 Jeffrey Bosboom Josh Brunner MIT CSAIL, Cambridge, MA, USA MIT CSAIL, Cambridge, MA, USA 4 Charlotte Chen Erik D. Demaine MIT, Cambridge, MA, USA MIT CSAIL, Cambridge, MA, USA 5 Rogers Epstein Adam Hesterberg MIT CSAIL, Cambridge, MA, USA Harvard University, Cambridge, MA, USA 6 Lior Hirschfeld William Hu MIT, Cambridge, MA, USA MIT, Cambridge, MA, USA 7 Jayson Lynch Sarah Scheffler MIT CSAIL, Cambridge, MA, USA Boston University, Boston, MA, USA 8 Lillian Zhang 9 MIT, Cambridge, MA, USA 10 Abstract 11 When can n given numbers be combined using arithmetic operators from a given subset of 12 {+, −, ×, ÷} to obtain a given target number? We study three variations of this problem of 13 Arithmetic Expression Construction: when the expression (1) is unconstrained; (2) has a specified 14 pattern of parentheses and operators (and only the numbers need to be assigned to blanks); or 15 (3) must match a specified ordering of the numbers (but the operators and parenthesization are 16 free). For each of these variants, and many of the subsets of {+, −, ×, ÷}, we prove the problem 17 NP-complete, sometimes in the weak sense and sometimes in the strong sense. Most of these proofs 18 make use of a rational function framework which proves equivalence of these problems for values in 19 rational functions with values in positive integers. 20 2012 ACM Subject Classification Theory of computation → Problems, reductions and completeness 21 Keywords and phrases Hardness, algebraic complexity, expression trees 22 Digital Object Identifier 10.4230/LIPIcs.ISAAC.2020.41 23 Related Version A full version of the paper is available on arXiv. -
The Relative Use of Formal and Informal Information in The
THE RELATIVE USE OF FORMAL AND INFORMAL INFORMATION IN THE EVALUATION OF INDIVIDUAL PERFORMANCE by GENE H. JOHNSON, B.B.A., M.S. in Acct. A DISSERTATION IN BUSINESS ADMINISTRATION Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY December, 1986 • //^,;¥ (c) 1986 Gene H. Johnson ACKNOWLEDGMENTS Funding for this research project was provided by the National Association of Accountants, and was especially beneficial in that it allowed the author to complete the project in a timely manner. Subjects for the project were provided by the research entity which must, for the sake of anonymity, remain unnamed. Nonetheless, their partici pation is greatly appreciated. Early conceptual development of the project was facilitated by a number of individuals, including the doctoral students and Professors Don Clancy and Frank Collins of Texas Tech University. Also helpful were the experiences of Louis Johnson, John Johnson, Sam Nichols, Darrell Adams, and Del Shumate. The members of the committee provided valuable guidance and support throughout the project; and although not a member of the committee. Professor Roy Howell provided valuable assistance with data analysis. Finally, Professor Donald K. Clancy served not only as committee chairman but also as a role model/mentor for the author. His participation made the project interesting, educational, and enjoyable. 11 CONTENTS ACKNOWLEDGMENTS ii ABSTRACT vi LIST OF TABLES viii LIST OF FIGURES ix I. INTRODUCTION AND BACKGROUND 1 Formal and Informal Information 2 Performance Evaluation 4 Purpose, Objectives, and Significance . 6 Organization 7 II. PREVIOUS STUDIES OF INFORMATION FOR PERFORMANCE EVALUATION 8 Goals and Goal-Directed Behavior 9 Early Studies on Performance Measures . -
An Architectural Description Language for Secure Multi-Agent Systems
An Architectural Description Language for Secure Multi-Agent Systems Haralambos Mouratidis1,a, Manuel Kolpb, Paolo Giorginic, Stephane Faulknerd aInnovative Informatics Group, School of Computing, IT and Engineering, University of East London, England b Information Systems Research Unit, Catholic University of Louvain (UCL), Belgium c Department. of Information and Communication Technology, University of Trento, Italy d Information Management Research Unit, University of Namur, Belgium Abstract. Multi-Agent Systems (MAS) architectures are gaining popularity for building open, distributed, and evolving information systems. Unfortunately, despite considerable work in the fields of software architecture and MAS during the last decade, few research efforts have aimed at defining languages for designing and formalising secure agent architectures. This paper proposes a novel Architectural Description Language (ADL) for describing Belief-Desire-Intention (BDI) secure MAS. We specify each element of our ADL using the Z specification language and we employ two example case studies: one to assist us in the description of the proposed language and help readers of the article to better understand the fundamentals of the language; and one to demonstrate its applicability. Keyword: Architectural Description Language, Multi-Agent Systems, Security, BDI Agent Model, Software Architecture 1 Corresponding Author: [email protected] 1 1. Introduction However, as the expectations of business stakeholders are changing day after day; and The characteristics and expectations of as the complexity of systems, information new application areas for the enterprise, and communication technologies and such as e-business, knowledge management, organisations is continually increasing in peer-to-peer computing, and web services, today’s dynamic environments; developers are deeply modifying information systems are expected to produce architectures that engineering. -
E.W. Dijkstra Archive: on the Cruelty of Really Teaching Computing Science
On the cruelty of really teaching computing science Edsger W. Dijkstra. (EWD1036) http://www.cs.utexas.edu/users/EWD/ewd10xx/EWD1036.PDF The second part of this talk pursues some of the scientific and educational consequences of the assumption that computers represent a radical novelty. In order to give this assumption clear contents, we have to be much more precise as to what we mean in this context by the adjective "radical". We shall do so in the first part of this talk, in which we shall furthermore supply evidence in support of our assumption. The usual way in which we plan today for tomorrow is in yesterday’s vocabulary. We do so, because we try to get away with the concepts we are familiar with and that have acquired their meanings in our past experience. Of course, the words and the concepts don’t quite fit because our future differs from our past, but then we stretch them a little bit. Linguists are quite familiar with the phenomenon that the meanings of words evolve over time, but also know that this is a slow and gradual process. It is the most common way of trying to cope with novelty: by means of metaphors and analogies we try to link the new to the old, the novel to the familiar. Under sufficiently slow and gradual change, it works reasonably well; in the case of a sharp discontinuity, however, the method breaks down: though we may glorify it with the name "common sense", our past experience is no longer relevant, the analogies become too shallow, and the metaphors become more misleading than illuminating. -
Should Your Specification Language Be Typed?
Should Your Specification Language Be Typed? LESLIE LAMPORT Compaq and LAWRENCE C. PAULSON University of Cambridge Most specification languages have a type system. Type systems are hard to get right, and getting them wrong can lead to inconsistencies. Set theory can serve as the basis for a specification lan- guage without types. This possibility, which has been widely overlooked, offers many advantages. Untyped set theory is simple and is more flexible than any simple typed formalism. Polymorphism, overloading, and subtyping can make a type system more powerful, but at the cost of increased complexity, and such refinements can never attain the flexibility of having no types at all. Typed formalisms have advantages too, stemming from the power of mechanical type checking. While types serve little purpose in hand proofs, they do help with mechanized proofs. In the absence of verification, type checking can catch errors in specifications. It may be possible to have the best of both worlds by adding typing annotations to an untyped specification language. We consider only specification languages, not programming languages. Categories and Subject Descriptors: D.2.1 [Software Engineering]: Requirements/Specifica- tions; D.2.4 [Software Engineering]: Software/Program Verification—formal methods; F.3.1 [Logics and Meanings of Programs]: Specifying and Verifying and Reasoning about Pro- grams—specification techniques General Terms: Verification Additional Key Words and Phrases: Set theory, specification, types Editors’ introduction. We have invited the following -
1 Logic, Language and Meaning 2 Syntax and Semantics of Predicate
Semantics and Pragmatics 2 Winter 2011 University of Chicago Handout 1 1 Logic, language and meaning • A formal system is a set of primitives, some statements about the primitives (axioms), and some method of deriving further statements about the primitives from the axioms. • Predicate logic (calculus) is a formal system consisting of: 1. A syntax defining the expressions of a language. 2. A set of axioms (formulae of the language assumed to be true) 3. A set of rules of inference for deriving further formulas from the axioms. • We use this formal system as a tool for analyzing relevant aspects of the meanings of natural languages. • A formal system is a syntactic object, a set of expressions and rules of combination and derivation. However, we can talk about the relation between this system and the models that can be used to interpret it, i.e. to assign extra-linguistic entities as the meanings of expressions. • Logic is useful when it is possible to translate natural languages into a logical language, thereby learning about the properties of natural language meaning from the properties of the things that can act as meanings for a logical language. 2 Syntax and semantics of Predicate Logic 2.1 The vocabulary of Predicate Logic 1. Individual constants: fd; n; j; :::g 2. Individual variables: fx; y; z; :::g The individual variables and constants are the terms. 3. Predicate constants: fP; Q; R; :::g Each predicate has a fixed and finite number of ar- guments called its arity or valence. As we will see, this corresponds closely to argument positions for natural language predicates.