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Grand Theft Auto V - Guia Não-Oficial
by Telma Rodrigues Joshua AbbottA popular saga Grand Theft Auto chegou à sua quinta edição, nomeadamente GTA 5, ou Grand Theft Auto Cinco. Se analisarmos a franquia Grand Theft Auto, veremos que o Grand Theft Auto V ocupa o 15º título global. Este incrível jogo, criado pela editora Rockstar Games, pode ser jogado na PS3 e Xbox 360 de 17 de setembro de 2013. Este guia, contém dicas que irão ajudar o jogador a explorar e a concretizar as missões deste incrível jogo. Assim como truques, mods e cheats para uma maior diversão. Bom jogo!
Grand Timely Topics in Software Engineering
by Jácome Cunha João P. Fernandes Ralf Lämmel João Saraiva Vadim ZaytsevThis tutorial volume includes the revised and extended tutorials (briefings) held at the 5th International Summer School on Grand Timely Topics in Software Engineering, GTTSE 2015, in Braga, Portugal, in August 2015. GTTSE 2015 applied a broader scope to include additional areas of software analysis, empirical research, modularity, and product lines. The tutorials/briefings cover probabilistic program analysis, ontologies in software engineering, empirical evaluation of programming and programming languages, model synchronization management of software product families, "people analytics" in software development, DSLs in robotics, structured program generation techniques, advanced aspects of software refactoring, and name binding in language implementation.
Grant Seeking in an Electronic Age (Technical Communication Ser.)
by Victoria M. Mikelonis Signe T. Betsinger Constance KampfThis guide teaches students and professionals a systematic process for researching, designing, writing, and submitting successful grant-seeking proposals. <P><P>Focusing on proposals submitted for government, foundation, and corporation funding, Grant Seeking in an Electronic Age leads the reader through a six-step grant-seeking process, from researching potential funders, to designing, writing and submitting a proposal that follows the funder's guidelines. Grounded in theory, but rooted in successful practice, it teaches students what really works–a third of students who submit proposals based on this text's approach get funded within a year. The text's guided discovery process provides a useful framework for novice writers while its thinking-planning exercises offer useful ways of organizing information and discovering what still need to be researched.
Granular Computing: Analysis and Design of Intelligent Systems (Industrial Electronics #70)
by Witold PedryczInformation granules, as encountered in natural language, are implicit in nature. To make them fully operational so they can be effectively used to analyze and design intelligent systems, information granules need to be made explicit. An emerging discipline, granular computing focuses on formalizing information granules and unifying them to create a coherent methodological and developmental environment for intelligent system design and analysis. Granular Computing: Analysis and Design of Intelligent Systems presents the unified principles of granular computing along with its comprehensive algorithmic framework and design practices. Introduces the concepts of information granules, information granularity, and granular computing Presents the key formalisms of information granules Builds on the concepts of information granules with discussion of higher-order and higher-type information granules Discusses the operational concept of information granulation and degranulation by highlighting the essence of this tandem and its quantification in terms of the associated reconstruction error Examines the principle of justifiable granularity Stresses the need to look at information granularity as an important design asset that helps construct more realistic models of real-world systems or facilitate collaborative pursuits of system modeling Highlights the concepts, architectures, and design algorithms of granular models Explores application domains where granular computing and granular models play a visible role, including pattern recognition, time series, and decision making Written by an internationally renowned authority in the field, this innovative book introduces readers to granular computing as a new paradigm for the analysis and synthesis of intelligent systems. It is a valuable resource for those engaged in research and practical developments in computer, electrical, industrial, manufacturing, and biomedical engineering. Building from fundamentals, the book is also suitable for readers from nontechnical disciplines where information granules assume a visible position.
Granular Knowledge Cube: An Expert Finder System for Knowledge Carriers (Fuzzy Management Methods)
by Alexander DenzlerThis book introduces a novel type of expert finder system that can determine the knowledge that specific users within a community hold, using explicit and implicit data sources to do so. Further, it details how this is accomplished by combining granular computing, natural language processing and a set of metrics that it introduces to measure and compare candidates’ suitability. The book describes profiling techniques that can be used to assess knowledge requirements on the basis of a given problem statement or question, so as to ensure that only the most suitable candidates are recommended. The book brings together findings from natural language processing, artificial intelligence and big data, which it subsequently applies to the context of expert finder systems. Accordingly, it will appeal to researchers, developers and innovators alike.
Granular-Relational Data Mining
by Piotr HońkoThis book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.
Granularities-Driven Hesitant Fuzzy Linguistic Decision Making (Studies in Fuzziness and Soft Computing #433)
by Zeshui Xu Yuanhang ZhengThis book introduces a state-of-the-art extension of fuzzy sets that is hesitant fuzzy linguistic term sets with granularity levels, and based on the fuzzy technique, several granularities-driven hesitant fuzzy linguistic decision-making methods are introduced to provide powerful tools to solve actual problems. Motivated from the idea of granular computing, the technique of hesitant fuzzy linguistic term sets with granularity levels is constructed, which not only brings flexibility and individuality for the linguistic model, but also provides a possibility to process a large amount of linguistic information in group decision-making efficiently and accurately. Thus, the researches on granularities-driven hesitant fuzzy linguistic decision making, can provide an effective way to solve practical decision-making problems based on complex linguistic information, and enrich the research system of decision-making and granular computing in theory and practice. In specific, this book introduces the construction of hesitant fuzzy linguistic term sets with granularity levels, and methods of handling attribute dependence, attribute reduction, single-objective group decision-making, and bi-objective group decision-making. The above decision-making methods are applied to the evaluation of medical and health management, and the effectiveness and advantages of the methods are verified by simulation comparison and analysis. Therefore, this book has not only important theoretical significance, but also broad application prospects.
Graph Algebras and Automata (Chapman & Hall/CRC Pure and Applied Mathematics)
by null Andrei KelarevGraph algebras possess the capacity to relate fundamental concepts of computer science, combinatorics, graph theory, operations research, and universal algebra. They are used to identify nontrivial connections across notions, expose conceptual properties, and mediate the application of methods from one area toward questions of the other four. After
Graph Algorithms: Practical Examples in Apache Spark and Neo4j
by Mark Needham Amy E. HodlerDiscover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection.Learn how graph analytics vary from conventional statistical analysisUnderstand how classic graph algorithms work, and how they are appliedGet guidance on which algorithms to use for different types of questionsExplore algorithm examples with working code and sample datasets from Spark and Neo4jSee how connected feature extraction can increase machine learning accuracy and precisionWalk through creating an ML workflow for link prediction combining Neo4j and Spark
Graph Algorithms for Data Science: With examples in Neo4j
by Tomaž BratanicPractical methods for analyzing your data with graphs, revealing hidden connections and new insights.Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don&’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It&’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You&’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. Foreword by Michael Hunger. About the technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you&’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's inside Creating knowledge graphs Node classification and link prediction workflows NLP techniques for graph construction About the reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the author Tomaž Bratanic works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Table of Contents PART 1 INTRODUCTION TO GRAPHS 1 Graphs and network science: An introduction 2 Representing network structure: Designing your first graph model PART 2 SOCIAL NETWORK ANALYSIS 3 Your first steps with Cypher query language 4 Exploratory graph analysis 5 Introduction to social network analysis 6 Projecting monopartite networks 7 Inferring co-occurrence networks based on bipartite networks 8 Constructing a nearest neighbor similarity network PART 3 GRAPH MACHINE LEARNING 9 Node embeddings and classification 10 Link prediction 11 Knowledge graph completion 12 Constructing a graph using natural language processing technique
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified
by Jeremy KubicaEnter the wonderful world of graph algorithms, where you&’ll learn when and how to apply these highly useful data structures to solve a wide range of fascinating (and fantastical) computational problems.Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you&’ll build a solid foundation for addressing graph problems in your own projects.Explore a rich landscape of cleverly constructed scenarios where:Hedge mazes illuminate depth-first searchUrban explorations demonstrate breadth-first searchIntricate labyrinths reveal bridges and articulation pointsStrategic planning illustrates bipartite matchingFrom fundamental graph structures to advanced topics, you will:Implement powerful algorithms, including Dijkstra&’s, A*, and Floyd-WarshallTackle puzzles and optimize pathfinding with newfound confidenceUncover real-world applications in social networks and transportation systemsDevelop robust intuition for when and why to apply specific graph techniquesDelve into topological sorting, minimum spanning trees, strongly connected components, and random walks. Confront challenges like graph coloring and the traveling salesperson problem.Prepare to view the world through the lens of graphs—where connections reveal insights and algorithms unlock new possibilities.
Graph Analysis and Visualization
by Richard Brath David JonkerWring more out of the data with a scientific approach toanalysis Graph Analysis and Visualization brings graph theory outof the lab and into the real world. Using sophisticated methods andtools that span analysis functions, this guide shows you how toexploit graph and network analytic techniques to enable thediscovery of new business insights and opportunities. Published infull color, the book describes the process of creating powerfulvisualizations using a rich and engaging set of examples fromsports, finance, marketing, security, social media, and more. Youwill find practical guidance toward pattern identification andusing various data sources, including Big Data, plus clearinstruction on the use of software and programming. The companionwebsite offers data sets, full code examples in Python, and linksto all the tools covered in the book.Science has already reaped the benefit of network and graphtheory, which has powered breakthroughs in physics, economics,genetics, and more. This book brings those proven techniques intothe world of business, finance, strategy, and design, helpingextract more information from data and better communicate theresults to decision-makers.Study graphical examples of networks using clear and insightfulvisualizationsAnalyze specifically-curated, easy-to-use data sets fromvarious industriesLearn the software tools and programming languages that extractinsights from dataCode examples using the popular Python programminglanguageThere is a tremendous body of scientific work on network andgraph theory, but very little of it directly applies to analystfunctions outside of the core sciences - until now. Writtenfor those seeking empirically based, systematic analysis methodsand powerful tools that apply outside the lab, Graph Analysisand Visualization is a thorough, authoritative resource.
Graph-Based Clustering and Data Visualization Algorithms
by Ágnes Vathy-Fogarassy János AbonyiThis work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.
Graph-Based Natural Language Processing and Information Retrieval
by Rada Mihalcea Dragomir RadevGraph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.
Graph-Based Representation and Reasoning: 27th International Conference on Conceptual Structures, ICCS 2022, Münster, Germany, September 12–15, 2022, Proceedings (Lecture Notes in Computer Science #13403)
by Tanya Braun Diana Cristea Robert JäschkeThis book constitutes the proceedings of the 27th International Conference on Conceptual Structures, ICCS 2022, held virtually in September 2022.The 7 full papers and 1 short paper presented were carefully reviewed and selected from 25 submissions. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts.
Graph-Based Representation and Reasoning: 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20–22, 2021, Proceedings (Lecture Notes in Computer Science #12879)
by Tanya Braun Marcel Gehrke Tom Hanika Nathalie HernandezThis book constitutes the proceedings of the 26th International Conference on Conceptual Structures, ICCS 2021, held virtually in September 2021.The 12 full papers and 4 short papers presented were carefully reviewed and selected from 25 submissions. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. The papers are organized in the following topical sections: applications of conceptual structures; theory on conceptual structures, and mining conceptual structures.
Graph-Based Representation and Reasoning: 23rd International Conference on Conceptual Structures, ICCS 2018, Edinburgh, UK, June 20-22, 2018, Proceedings (Lecture Notes in Computer Science #10872)
by Peter Chapman Dominik Endres Nathalie PernelleThis book constitutes the proceedings of the 23rd International Conference on Conceptual Structures, ICCS 2018, held in Edinburgh, UK, in June 2018.The 10 full papers, 2 short papers and 2 posters presented were carefully reviewed and selected from 21 submissions. They are organized in the following topical sections: graph- and concept-based inference; computer- human interaction and human cognition; and graph visualization.
Graph-Based Representation and Reasoning: 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1–4, 2019, Proceedings (Lecture Notes in Computer Science #11530)
by Dominik Endres Mehwish Alam Diana ŞotropaThis book constitutes the proceedings of the 24th International Conference on Conceptual Structures, ICCS 2019, held in Marburg, Germany, in July 2019. The 14 full papers and 6 short papers presented were carefully reviewed and selected from 29 submissions. The proceedings also include one of the two invited talks. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. ICCS 2019's theme was entitled "Graphs in Human and Machine Cognition."
Graph-Based Representation and Reasoning
by Ollivier Haemmerlé Gem Stapleton Catherine Faron ZuckerThis book constitutes the proceedings of the 22th International Conference on Conceptual Structures, ICCS 2016, held in Annecy, France, in July 2016. The 14 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They are organized around the following topical sections: time representation; graphs and networks; formal concept analysis; ontologies and linked data.
Graph-Based Representation and Reasoning: 28th International Conference on Conceptual Structures, ICCS 2023, Berlin, Germany, September 11–13, 2023, Proceedings (Lecture Notes in Computer Science)
by Manuel Ojeda-Aciego Kai Sauerwald Robert JäschkeThis book constitutes the refereed deadline proceedings of the 28th International Conference on Graph-Based Representation and Reasoning, ICCS 2023, held in Berlin, Germany, during September 11–13, 2023.The 9 full papers, 5 short papers and 4 Posters are included in this book were carefully reviewed and selected from 32 submissions. They were organized in topical sections as follows: Complexity and Database Theory, Formal Concept Analysis: Theoretical Advances, Formal Concept Analysis: Applications, Modelling and Explanation, Semantic Web and Graphs, Posters.
Graph-Based Representations in Pattern Recognition: 12th IAPR-TC-15 International Workshop, GbRPR 2019, Tours, France, June 19–21, 2019, Proceedings (Lecture Notes in Computer Science #11510)
by Donatello Conte Jean-Yves Ramel Pasquale FoggiaThis book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2019, held in Tours, France, in June 2019.The 22 full papers included in this volume together with an invited talk were carefully reviewed and selected from 28 submissions. The papers discuss research results and applications at the intersection of pattern recognition, image analysis, and graph theory. They cover topics such as graph edit distance, graph matching, machine learning for graph problems, network and graph embedding, spectral graph problems, and parallel algorithms for graph problems.
Graph-Based Representations in Pattern Recognition: 11th IAPR-TC-15 International Workshop, GbRPR 2017, Anacapri, Italy, May 16–18, 2017, Proceedings (Lecture Notes in Computer Science #10310)
by Pasquale Foggia Cheng-Lin Liu Mario VentoThis book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2017, held in Anacapri, Italy, in May 2017. The 25 full papers and 2 abstracts of invited papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers discuss research results and applications in the intersection of pattern recognition, image analysis, graph theory, and also the application of graphs to pattern recognition problems in other fields like computational topology, graphic recognition systems and bioinformatics.
Graph-Based Representations in Pattern Recognition: 13th IAPR-TC-15 International Workshop, GbRPR 2023, Vietri sul Mare, Italy, September 6–8, 2023, Proceedings (Lecture Notes in Computer Science #14121)
by Mario Vento Pasquale Foggia Donatello Conte Vincenzo CarlettiThis book constitutes the refereed proceedings of the 13th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2023, which took place in Vietri sul Mare, Italy, in September 2023.The 16 full papers included in this book were carefully reviewed and selected from 18 submissions. They were organized in topical sections on graph kernels and graph algorithms; graph neural networks; and graph-based representations and applications.
Graph-Based Social Media Analysis (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Ioannis PitasFocused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear alge
Graph Data Mining: Algorithm, Security and Application (Big Data Management)
by Qi Xuan Zhongyuan Ruan Yong MinGraph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic – the security of graph data mining – and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.