07194nam 22007695 450 99646559530331620200706155459.03-540-37035-810.1007/11811220(CKB)1000000000233064(SSID)ssj0000318587(PQKBManifestationID)11239937(PQKBTitleCode)TC0000318587(PQKBWorkID)10310484(PQKB)11558151(DE-He213)978-3-540-37035-2(MiAaPQ)EBC3068181(PPN)123137144(EXLCZ)99100000000023306420100301d2006 u| 0engurnn|008mamaatxtccrKnowledge Science, Engineering and Management[electronic resource] First International Conference, KSEM 2006, Guilin, China, August 5-8, 2006, Proceedings /edited by Jérôme Lang, Fangzhen Lin, Ju Wang1st ed. 2006.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2006.1 online resource (XVI, 664 p.) Lecture Notes in Artificial Intelligence ;4092Bibliographic Level Mode of Issuance: Monograph3-540-37033-1 Includes bibliographical references and index.Invited Talks -- On Representational Issues About Combinations of Classical Theories with Nonmonotonic Rules -- Towards a Software/Knowware Co-engineering -- Modeling and Evaluation of Technology Creation Process in Academia -- Knowledge Management Systems (KMS) Continuance in Organizations: A Social Relational Perspective -- Regular Papers -- Modelling the Interaction Between Objects: Roles as Affordances -- Knowledge Acquisition for Diagnosis in Cellular Networks Based on Bayesian Networks -- Building Conceptual Knowledge for Managing Learning Paths in e-Learning -- Measuring Similarity in the Semantic Representation of Moving Objects in Video -- A Case Study for CTL Model Update -- Modeling Strategic Beliefs with Outsmarting Belief Systems -- Marker-Passing Inference in the Scone Knowledge-Base System -- Hyper Tableaux — The Third Version -- A Service-Oriented Group Awareness Model and Its Implementation -- An Outline of a Formal Ontology of Genres -- An OWL-Based Approach for RBAC with Negative Authorization -- LCS: A Linguistic Combination System for Ontology Matching -- Framework for Collaborative Knowledge Sharing and Recommendation Based on Taxonomic Partial Reputations -- On Text Mining Algorithms for Automated Maintenance of Hierarchical Knowledge Directory -- Using Word Clusters to Detect Similar Web Documents -- Construction of Concept Lattices Based on Indiscernibility Matrices -- Selection of Materialized Relations in Ontology Repository Management System -- Combining Topological and Directional Information: First Results -- Measuring Conflict Between Possibilistic Uncertain Information Through Belief Function Theory -- WWW Information Integration Oriented Classification Ontology Integrating Approach -- Configurations for Inference Between Causal Statements -- Taking Levi Identity Seriously: A Plea for Iterated Belief Contraction -- Description and Generation of Computational Agents -- Knowledge Capability: A Definition and Research Model -- Quota-Based Merging Operators for Stratified Knowledge Bases -- Enumerating Minimal Explanations by Minimal Hitting Set Computation -- Observation-Based Logic of Knowledge, Belief, Desire and Intention -- Repairing Inconsistent XML Documents -- A Framework for Automated Test Generation in Intelligent Tutoring Systems -- A Study on Knowledge Creation Support in a Japanese Research Institute -- Identity Conditions for Ontological Analysis -- Knowledge Update in a Knowledge-Based Dynamic Scheduling Decision System -- Knowledge Contribution in the Online Virtual Community: Capability and Motivation -- Effective Large Scale Ontology Mapping -- A Comparative Study on Representing Units in Chinese Text Clustering -- A Description Method of Ontology Change Management Using Pi-Calculus -- On Constructing Environment Ontology for Semantic Web Services -- Knowledge Reduction in Incomplete Systems Based on ?–Tolerance Relation -- An Extension Rule Based First-Order Theorem Prover -- An Extended Meta-model for Workflow Resource Model -- Knowledge Reduction Based on Evidence Reasoning Theory in Ordered Information Systems -- A Novel Maximum Distribution Reduction Algorithm for Inconsistent Decision Tables -- An ICA-Based Multivariate Discretization Algorithm -- An Empirical Study of What Drives Users to Share Knowledge in Virtual Communities -- A Method for Evaluating the Knowledge Transfer Ability in Organization -- Information Extraction from Semi-structured Web Documents -- Si-SEEKER: Ontology-Based Semantic Search over Databases -- Efficient Computation of Multi-feature Data Cubes -- NKIMathE – A Multi-purpose Knowledge Management Environment for Mathematical Concepts -- Linguistic Knowledge Representation and Automatic Acquisition Based on a Combination of Ontology with Statistical Method -- Toward Formalizing Usefulness in Propositional Language.Lecture Notes in Artificial Intelligence ;4092Data structures (Computer science)Artificial intelligenceInformation storage and retrievalApplication softwareDatabase managementPattern recognitionData Structures and Information Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/I15009Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Information Storage and Retrievalhttps://scigraph.springernature.com/ontologies/product-market-codes/I18032Information Systems Applications (incl. Internet)https://scigraph.springernature.com/ontologies/product-market-codes/I18040Database Managementhttps://scigraph.springernature.com/ontologies/product-market-codes/I18024Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XData structures (Computer science).Artificial intelligence.Information storage and retrieval.Application software.Database management.Pattern recognition.Data Structures and Information Theory.Artificial Intelligence.Information Storage and Retrieval.Information Systems Applications (incl. Internet).Database Management.Pattern Recognition.005.741Lang Jérômeedthttp://id.loc.gov/vocabulary/relators/edtLin Fangzhenedthttp://id.loc.gov/vocabulary/relators/edtWang Juedthttp://id.loc.gov/vocabulary/relators/edtBOOK996465595303316Knowledge Science, Engineering and Management772454UNISA05978nam 2201213 450 991081691620332120230629171856.00-691-16262-X1-4008-5266-810.1515/9781400852666(CKB)2670000000572419(SSID)ssj0001368176(PQKBManifestationID)12508227(PQKBTitleCode)TC0001368176(PQKBWorkID)11445542(PQKB)11183666(StDuBDS)EDZ0001755601(OCoLC)893909903(MdBmJHUP)muse43211(DE-B1597)454042(OCoLC)984545576(DE-B1597)9781400852666(Au-PeEL)EBL1753617(CaPaEBR)ebr10960637(CaONFJC)MIL653542(OCoLC)894169662(MiAaPQ)EBC1753617(EXLCZ)99267000000057241920141107h20152015 uy 0engurcnu||||||||txtccrThe impression of influence legislator communication, representation, and democratic accountability /Justin Grimmer, Sean J. Westwood, and Solomon MessingPilot project. eBook available to selected US libraries onlyPrinceton, New Jersey ;Oxfordshire, England :Princeton University Press,2015.©20151 online resource (221 pages) illustrations, tablesBibliographic Level Mode of Issuance: Monograph1-322-22262-2 0-691-16261-1 Includes bibliographical references and index.Frontmatter -- Contents -- List of Illustrations -- List of Tables -- Acknowledgments -- 1. Representation, Spending, and the Personal Vote -- 2. Solving the Representative's Problem and Creating the Representative's Opportunity -- 3. How Legislators Create an Impression of Influence -- 4. Creating an Impression, Not Just Increasing Name Recognition -- 5. Cultivating an Impression of Influence with Actions and Small Expenditures -- 6. Credit, Deception, and Institutional Design -- 7. Criticism and Credit: How Deficit Implications Undermine Credit Allocation -- 8. Representation and the Impression of Influence -- 9. Text as Data: Methods Appendix -- Bibliography -- IndexConstituents often fail to hold their representatives accountable for federal spending decisions-even though those very choices have a pervasive influence on American life. Why does this happen? Breaking new ground in the study of representation, The Impression of Influence demonstrates how legislators skillfully inform constituents with strategic communication and how this facilitates or undermines accountability. Using a massive collection of Congressional texts and innovative experiments and methods, the book shows how legislators create an impression of influence through credit claiming messages.Anticipating constituents' reactions, legislators claim credit for programs that elicit a positive response, making constituents believe their legislator is effectively representing their district. This spurs legislators to create and defend projects popular with their constituents. Yet legislators claim credit for much more-they announce projects long before they begin, deceptively imply they deserve credit for expenditures they had little role in securing, and boast about minuscule projects. Unfortunately, legislators get away with seeking credit broadly because constituents evaluate the actions that are reported, rather than the size of the expenditures.The Impression of Influence raises critical questions about how citizens hold their political representatives accountable and when deception is allowable in a democracy.LegislatorsUnited StatesPublic opinionGovernment spending policyUnited StatesPublic opinionCommunication in politicsUnited StatesObama administration.Republican activists.Tea Party movement.Text as Data.accountability.antispending rhetoric.appropriations process.budget criticism.bureaucrats.congressional credit claiming.credit allocation.credit claiming messages.credit claiming.credit-claiming messages.credit-claiming rates.credit-claiming strategies.credit.deception.democracy.democratic competence.expenditure.federal expenditures.federal funds.federal spending.government spending.grant decisions.hand-coded documents.hand-coded labels.influence.legislators.linguistic deception.name recognition.nonpartisan reputation.particularistic projects.partisan reputation.personal vote.political representation.press releases.spending.statistical modeling.systematic deception.transparent communication.LegislatorsPublic opinion.Government spending policyPublic opinion.Communication in politics328.73Grimmer Justin1685949Westwood Sean J.Messing SolomonMiAaPQMiAaPQMiAaPQBOOK9910816916203321The impression of influence4058506UNINA12032nam 22006733 450 991091568090332120230708060220.01-68392-861-X1-68392-860-110.1515/9781683928614(MiAaPQ)EBC30620241(Au-PeEL)EBL30620241(BIP)094761529(DE-B1597)658511(DE-B1597)9781683928614(CKB)27483040200041(FR-PaCSA)88949217(EXLCZ)992748304020004120230708d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPython Programming Using Problem Solving1st ed.Bloomfield :Mercury Learning & Information,2023.©2023.1 online resource (601 pages)1-68392-862-8 Cover -- Half-Title -- Title -- Copyright -- Dedication -- Content -- Preface -- Section I: Algorithmic Problem-Solving and Python Fundamentals -- Chapter 1: Algorithmic Problem-Solving -- 1.1 Introduction -- 1.2 Definition and Characteristics -- 1.3 Notations: Pseudocode and Flow Chart -- 1.4 Strategies for Problem-Solving: Recursion Versus Iteration -- 1.5 Asymptotic Notation -- 1.6 Complexity -- 1.7 Illustrations -- 1.7.1 Minimum in a List -- 1.7.2 Insert a Card in a Pack of Cards (Or Insert an element ina sorted list). There are ten cards in the pack, numbered from 1 to 10. -- 1.7.3 Guess a Number in a Given Range -- 1.7.4 Tower of Hanoi -- 1.8 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Application -- Chapter 2: Introduction to Python -- 2.1 Introduction -- 2.2 Features of Python -- 2.2.1 Easy -- 2.2.2 Type and Run -- 2.2.3 Syntax -- 2.2.4 Mixing -- 2.2.5 Dynamic Typing -- 2.2.6 Built-in Object Types -- 2.2.7 Numerous Libraries and Tools -- 2.2.8 Portable -- 2.2.9 Free -- 2.3 The Paradigms -- 2.3.1 Procedural -- 2.3.2 Object-Oriented -- 2.3.3 Functional -- 2.4 Chronology and Uses -- 2.4.1 Chronology -- 2.4.2 Uses -- 2.5 Installation of Anaconda -- 2.6 Implementation of an Algorithm: Statement, State, Control Blocks, and Functions -- 2.6.1 Statement -- 2.6.2 State -- 2.6.3 Control Flow -- 2.7 Conclusion -- Glossary -- Points to Remember -- Resources -- Exercises -- Multiple Choice Questions -- Theory -- Chapter 3: Fundamentals -- 3.1 Introduction -- 3.2 Basic Input Output -- 3.2.1 Print Function -- 3.2.2 Input -- 3.3 Running a Program -- 3.3.1 Using the Command Prompt -- 3.3.2 Executing Programs Written in .py Files -- 3.3.3 Using Anaconda Navigator -- 3.4 The Jupyter Notebook -- 3.5 Value Type and Reference Type -- 3.6 Tokens, Keywords, and Identifiers -- 3.6.1 Python Keywords.3.6.2 Python Identifiers -- 3.6.3 Python Escape Sequence -- 3.7 Statements -- 3.7.1 Expression Statement -- 3.7.2 Assignment Statements -- 3.7.3 The Assert Statements -- 3.7.4 The Pass Statements -- 3.7.5 The Control Statements -- 3.8 Comments -- 3.9 Operators -- 3.10 Types and Examples of Operators -- 3.10.1 Arithmetic Operators -- 3.10.2 String Operators -- 3.10.3 Comparison Operators -- 3.10.4 Assignment Operators -- 3.10.5 Logical Operators -- 3.10.6 Priority of Operators -- 3.11 Basic Data Types -- 3.11.1 Integer -- 3.11.2 Float -- 3.11.3 String -- 3.12 Conclusion -- Exercises -- Multiple Choice Questions -- Theory -- Explore -- Section II: Procedural Programming -- Chapter 4: Conditional Statements -- 4.1 Introduction -- 4.2 "If," If-Else, and If-Elif-Else Constructs -- 4.3 The If-Elif-Else Ladder -- 4.4 Logical Operators -- 4.5 The Ternary Operator -- 4.6 The Get Construct -- 4.7 Examples -- 4.8 Summary -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Programming Exercises -- Chapter 5: Looping -- 5.1 Introduction -- 5.2 While -- 5.3 Patterns -- 5.4 Nesting and Applications of Loops in Lists -- 5.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Programming Exercises -- Chapter 6: Functions -- 6.1 Introduction -- 6.2 Features of a Function -- 6.2.1 Modular Programming -- 6.2.2 Reusability of Code -- 6.2.3 Manageability -- 6.2.3.1 Easy debugging -- 6.2.3.2 Efficient -- 6.3 Basic Terminology -- 6.3.1 Name of a Function -- 6.3.2 Arguments -- 6.3.3 Return Value -- 6.4 Definition and Invocation -- 6.4.1 Working -- 6.5 Types of Function -- 6.5.1 Arguments: Types of Arguments -- 6.6 Implementing Search -- 6.7 Scope -- 6.8 Recursion -- 6.8.1 Rabbit Problem -- 6.8.2 Disadvantages of Using Recursion -- 6.9 Conclusion -- Glossary -- Points to Remember -- Exercises.Multiple Choice Questions -- Programming Exercises -- Questions Based on Recursion -- Theory -- Extra Questions -- Chapter 7: File Handling -- 7.1 Introduction -- 7.2 The File Handling Mechanism -- 7.3 The Open Function and File Access Modes -- 7.4 Python Functions for File Handling -- 7.4.1 The Essential Ones -- 7.4.2 The OS Methods -- 7.4.3 Miscellaneous Functions and File Attributes -- 7.5 Command Line Arguments -- 7.6 Implementation and illustrations -- 7.7 Conclusion -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 8: Lists, tuple, and Dictionar -- 8.1 Introduction -- 8.2 Lists -- 8.2.1 Accessing Elements: Indexing and Slicing -- 8.2.2 Mutability -- 8.2.3 Operators -- 8.2.4 Traversal -- 8.2.5 Functions -- 8.3 Tuple -- 8.3.1 Accessing Elements of a Tuple -- 8.3.2 Nonmutability -- 8.3.3 Operators -- 8.3.4 Traversal -- 8.3.5 Functions -- 8.4 Associate Arrays and Dictionaries -- 8.4.1 Displaying Elements of a Dictionary -- 8.4.2 Some Important Functions of Dictionaries -- 8.4.2.1 The len function returns the number of elements in a given dictionary. -- 8.4.2.2 The max function returns the key with maximum value. If the key is a string, then the value in the lexicographic ordering would be returned. -- 8.4.2.3 The min function returns the key with minimum value. If the key is a string, then the value in the lexicographic ordering would be returned. -- 8.4.2.4 The sorted function would sort the elements of a given dictionary by their keys. If the keys are strings then lexicographic ordering would be followed. -- 8.4.2.5 The pop function takes out the element with the given key from the dictionary. -- 8.4.3 Input from the User -- 8.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises.Chapter 9: Iterations, Generators, and Comprehensions -- 9.1 Introduction -- 9.2 The Power of "For -- 9.3 Iterator -- 9.4 Defining an Iterable Object -- 9.5 Generators -- 9.6 Comprehensions -- 9.7 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 10: Strings -- 10.1 Introduction -- 10.2 Loops Revised -- 10.3 String Operators -- 10.3.1 The Concatenation Operator (+) -- 10.3.2 The Replication Operator (*) -- 10.3.3 The Membership Operator -- 10.4 In-Built Functions -- 10.4.1 len() -- 10.4.2 Capitalize() -- 10.4.3 Find() -- 10.4.4 Count -- 10.4.5 endswith() -- 10.4.6 encode -- 10.4.7 decode -- 10.4.8 Miscellaneous Functions -- 10.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Section III: Object-Oriented Programming -- Chapter 11: Introduction to Object-Oriented Paradigm -- 11.1 Introduction -- 11.2 Creating New Types -- 11.3 Attributes and Functions -- 11.3.1 Attributes -- 11.3.2 Functions -- 11.4 Elements of Object-Oriented Programming -- 11.4.1 Class -- 11.4.2 Object -- 11.4.3 Encapsulation -- 11.4.4 Data Hiding -- 11.4.5 Inheritance -- 11.4.6 Polymorphism -- 11.4.7 Reusability -- 11.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Explore and Design -- Chapter 12: Classes and Objects -- 12.1 Introduction to Classes -- 12.2 Defining a Class -- 12.3 Creating an Object -- 12.4 Scope of Data Members -- 12.5 Nesting -- 12.6 Constructor -- 12.7 Multiple __Init__(s) -- 12.8 Destructors -- 12.9 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 13: Inheritance -- 13.1 Introduction to Inheritance and Composition -- 13.1.1 Inheritance and Methods -- 13.1.2 Composition.13.2 Inheritance: Importance and Types -- 13.2.1 Need for Inheritance -- 13.2.2 Types of Inheritance -- 13.2.2.1 Simple inheritance -- 13.2.2.2 Hierarchical inheritance -- 13.2.2.3 Multilevel inheritance -- 13.2.2.4 Multiple inheritance and hybrid inheritance -- 13.3 Methods -- 13.3.1 Bound Methods -- 13.3.2 Unbound Method -- 13.3.3 Methods are Callable Objects -- 13.3.4 The Importance and Usage of Super -- 13.3.5 Calling the Base Class Function Using Super -- 13.4 Search in Inheritance Tree -- 13.5 Class Interface and Abstract Classes -- 13.6 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 14: Operator Overloading -- 14.1 Introduction -- 14.2 __Init__ Revisited -- 14.2.1 Overloading __init__(Sort of) -- 14.3 Methods for Overloading Binary Operators -- 14.4 Overloading Binary Operators: The Fraction Example -- 14.5 Overloading the += Operator -- 14.6 Overloading the &gt -- and &lt -- Operators -- 14.7 Overloading the __Bool__ Operator: Precedence of __Bool__ Over __Len__ -- 14.8 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 15: Exception Handling -- 15.1 Introduction -- 15.2 Importance and Mechanism -- 15.2.1 An Example of Try/Except -- 15.2.2 Manually Raising Exceptions -- 15.3 Build-in Exceptions in Python -- 15.4 The Process -- 15.4.1 Example -- 15.4.2 Exception Handling: Try/Except -- 15.4.3 Raising Exceptions -- 15.5 Crafting User Defined Exceptions -- 15.6 An Example of Exception Handling -- 15.7 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Section IV: Numpy, Pandas, and Matplotlib -- Chapter 16: Numpy-I -- 16.1 Introduction -- 16.2 Fundamentals.16.2.1 Similarity and Differences Between a List and a NumPy Array.Python is a robust, procedural, object-oriented, and functional language. The features of the language make it valuable for web development, game development, business, and scientific programming. This book deals with problem-solving and programming in Python. It concentrates on the development of efficient algorithms, the syntax of the language, and the ability to design programs in order to solve problems. In addition to standard Python topics, the book has extensive coverage of NumPy, data visualization, and Matplotlib. Numerous types of exercises, including theoretical, programming, and multiple-choice, reinforce the concepts covered in each chapter. FEATURES:Concentrates on the development of efficient algorithms, the syntax of the language, and theability to design programs in order to solve problemsFeatures both standard Python topics and also extensive coverage of NumPy, data visualization, and Matplotlib problem-solving techniquesPython (Computer program language)COMPUTERS / GeneralbisacshMatplotlib.NumPy.Pandas.algorithm.business communication.computer science.engineering.programming.science.Python (Computer program language).COMPUTERS / General.005.133Bhasin Harsh1778243MiAaPQMiAaPQMiAaPQBOOK9910915680903321Python Programming Using Problem Solving4301007UNINA01085nas 2200385 c 450 991089602120332120260218113020.0(DE-599)ZDB2691451-7(OCoLC)1368751920(DE-101)1027950078(CKB)4910000000549037(DE-599)2691451-7(EXLCZ)99491000000054903720121119b18171824 |y |gerur|||||||||||txtrdacontentcrdamediacrrdacarrierAllgemeine musikalische Zeitungmit besonderer Rücksicht auf den österreichischen KaiserstaatWienSteiner1817-1824WienSteiner und Comp.WienStraussOnline-RessourceWiener allgemeine musikalische ZeitungZeitunggnd-content0707800355DE-1019001JOURNAL9910896021203321Allgemeine musikalische Zeitung4437704UNINA