Tabla de Contenidos: “…EvoSuite -- 4.4.2. SAGE and Project Springfield -- 4.4.3. Driller -- 4.4.4. Mayhem -- 5. …”

Tabla de Contenidos: “…Intro -- Preface -- Organization -- Invited Talks -- How to Solve Math Problems Without Talent -- Bridging Formal Mathematics and Software Verification -- The Art of SMT Solving -- Contents - Part II -- Concurrency -- The VerCors Verifier: A Progress Report -- 1 Introduction -- 2 New and Improved Language Support -- 2.1 Improved Existing Language Support -- 2.2 Newly Supported Frameworks -- 2.3 Programming Languages Encoded into VerCors -- 3 VerCors Implementation Changes -- 4 Deriving Verified, Optimised Programs -- 5 Case Studies -- 5.1 Tunnel Control Software Components -- 5.2 Verification of Red-Black Trees and their Parallel Merge -- 5.3 GPU Case Studies -- 5.4 Student Projects -- 6 Conclusions, Related Work and Future Work -- References -- Parsimonious Optimal Dynamic Partial Order Reduction -- 1 Introduction -- 2 Main Concepts -- 3 Programs, Executions, and Equivalence -- 4 Design of the POP Algorithm -- 4.1 Parsimonious Race Reversals -- 4.2 The Parsimonious-OPtimal DPOR (POP) Algorithm -- 4.3 Parsimonious Sleep Set Characterization -- 5 Correctness and Space Complexity -- 6 Implementation and Evaluation -- 7 Related Work -- 8 Conclusion -- References -- Collective Contracts for Message-Passing Parallel Programs -- 1 Introduction -- 2 A Theory of Collective Contracts -- 2.1 Language -- 2.2 Semantics -- 2.3 Collective Correctness -- 2.4 Simulation -- 3 Collective Contracts for C/MPI -- 3.1 Background from MPI -- 3.2 Contract Structure -- 4 Evaluation -- 4.1 Collective Contract Examples -- 4.2 Bounded Verification of Collective Contracts -- 5 Related Work -- 6 Discussion -- References -- Distributed Systems -- mypyvy: A Research Platform for Verification of Transition Systems in First-Order Logic -- 1 Introduction -- 2 Modeling Language -- 2.1 Benchmarks -- 3 Satisfiability-Based Queries -- 3.1 Queries -- 3.2 Counterexamples…”

Tabla de Contenidos: “…5.6.1 Step-efficient parallel scan operation -- 5.6.2 Work-efficient parallel scan operation -- 5.6.3 Parallel scan operations for large arrays -- 5.7 Parallel global sum: Addressing the problem of associativity -- 5.8 Future of parallel algorithm research -- 5.9 Further explorations -- 5.9.1 Additional reading -- 5.9.2 Exercises -- Summary -- Part 2 CPU: The parallel workhorse -- 6 Vectorization: FLOPs for free -- 6.1 Vectorization and single instruction, multiple data (SIMD) overview -- 6.2 Hardware trends for vectorization -- 6.3 Vectorization methods -- 6.3.1 Optimized libraries provide performance for little effort -- 6.3.2 Auto-vectorization: The easy way to vectorization speedup (most of the time1) -- 6.3.3 Teaching the compiler through hints: Pragmas and directives -- 6.3.4 Crappy loops, we got them: Use vector intrinsics -- 6.3.5 Not for the faint of heart: Using assembler code for vectorization -- 6.4 Programming style for better vectorization -- 6.5 Compiler flags relevant for vectorization for various compilers -- 6.6 OpenMP SIMD directives for better portability -- 6.7 Further explorations -- 6.7.1 Additional reading -- 6.7.2 Exercises -- Summary -- 7 OpenMP that performs -- 7.1 OpenMP introduction -- 7.1.1 OpenMP concepts -- 7.1.2 A simple OpenMP program -- 7.2 Typical OpenMP use cases: Loop-level, high-level, and MPI plus OpenMP -- 7.2.1 Loop-level OpenMP for quick parallelization -- 7.2.2 High-level OpenMP for better parallel performance -- 7.2.3 MPI plus OpenMP for extreme scalability -- 7.3 Examples of standard loop-level OpenMP -- 7.3.1 Loop level OpenMP: Vector addition example -- 7.3.2 Stream triad example -- 7.3.3 Loop level OpenMP: Stencil example -- 7.3.4 Performance of loop-level examples -- 7.3.5 Reduction example of a global sum using OpenMP threading -- 7.3.6 Potential loop-level OpenMP issues…”

“…What You Will Learn Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty Generate complex and simple random data to create a wide variety of test cases that can be codified into tests Develop models iteratively, even when using a third-party library Quantify model quality to enable collaboration and rapid iteration Adopt simpler approaches to common machine learning algorithms Take behaviour-driven development principles to articulate test intent In Detail Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. …”

Tabla de Contenidos: “…7.5 Applications of Cognitive Computing -- 7.5.1 Chatbots -- 7.5.2 Sentiment Analysis -- 7.5.3 Face Detection -- 7.5.4 Risk Assessment -- 7.6 Future of Cognitive Computing -- 7.7 Conclusion -- References -- 8 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems -- 8.1 Cyber Physical Systems -- 8.2 Introduction: The Four Phases of Industrial Revolution -- 8.3 System -- 8.4 Autonomous Automobile System -- 8.4.1 The Timeline -- 8.5 Robotic System -- 8.6 Mechatronics -- References -- 9 Role of Recent Technologies in Cognitive Systems -- 9.1 Introduction -- 9.1.1 Definition and Scope of Cognitive Computing -- 9.1.2 Architecture of Cognitive Computing -- 9.1.3 Features and Limitations of Cognitive Systems -- 9.2 Natural Language Processing for Cognitive Systems -- 9.2.1 Role of NLP in Cognitive Systems -- 9.2.2 Linguistic Analysis -- 9.2.3 Example Applications Using NLP With Cognitive Systems -- 9.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems -- 9.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation -- 9.3.2 How to Represent Knowledge in Cognitive Systems? …”

Tabla de Contenidos: “…(Comprehensive Office Building, Zhujiajiao Administration Center, Quingpu, Shangai, China, 2004-06), p. 334 ; PAULO MENDES DA ROCHA (Our Lady of the Conception Chapel, Recife, Pernambuco, Brazil, 2004-06), p. 340 ; CORINNA MENN (Conn Viewinf Platform, Flims, Switzerland, 2005-06), p. 346 ; MERKX + GIROD ARCHITECTEN (Selexyz Dominicanen Bookstore, Maastricht, The Netherlands, 2005-07), p. 352 ; JOSÉ RAFAEL MONEO (Prado Museum Extension, Madrid, Spain, 2001-07), p. 358 ; MORPHOSIS (Hypo Alpe-Adria Bank Headquarters, Udine, Italy, 2004-06), p. 364 ; MVRDV (GYRE Building, Shibuya-ku, Tokyo, Japan, 2006-07), p. 372 ; MANFREDI NICOLETTI (New Arezzo Hall of Justice, Arezzo, Italy, 2001-07), p. 378 ; VALERIO OLGIATI (Atelier Bardill, Scharans, Switzerland, 2006-07), p. 382 ; STUDIO PEI – ZHU (Digital Beijing, Control Center for the 2008 Olympics, Beijing, China, 2005-08), p. 386 ; RENZO PIANO (The New York Times Building, New York, New York, USA, 2005-07), p. 394 ; PTW ARCHITECTS (The Watercube, National Swimming Center, Beijing, China, 2003-08), p. 404 ; STUDIO ARNE QUINZE (Uchronia Message out of the Future, Burning Man Festival, Black Rock City, Black Rock Desert, Nevada, USA, 2006), p. 412 ; RCR ARQUITECTES (The Edge, Bussines Bay, Dubai, United Arab Emirates, 2008-), p. 418 ; ROJKIND ARQUITECTOS (Nestlé Chocolate Museum (Phase I), Toluca, Mexico City, Mexico, 2007), p. 424 ; MARC ROLINET (Chapel of the Deaconesses of Reuilly, Versailles, France, 2004-07), p. 430 ; HANS-JORG RUCH (Chesa Madalena, Zuoz, Switzerland, 2001-02), p. 438 ; SANAA/SEJIMA + NISHIZAWA (De Kunstlinie, Theater and Cultural Center, Almere, The Netherlands, 2004-07 / New Museum of Contemporary Art, New York, New York, USA, 2005-07), p. 442 ; THOMAS SCHÜTTE (Model for a Hotel, Fourth Plinth, Trafalgar Square, London, UK, 2007-08), p. 456 ; SCOPE CLEAVER (Princeton University Gallery of the Arts, Princeton University (120, 204, 24), “Second Life”, 2007), p. 460 ; ÁLVARO SIZA VIEIRA (Adega Mayor Winery, Argamassas Estate – Campo Maior, Portugal, 2005-06 / Iberê Camargo Foundation, Porto Alegre, Rio Grande do Sul, Brazil, 1998-2008), p. 466 ; SNØHETTA (Oslo Opera House, Oslo, Norway, 2003-08), p. 484 ; TONKIN LIU (Singing Ringing Tree, Crown Point, Burnley, UK, 2005-06), p. 492 ; BERNARD TSCHUMI (Zénith Concert Hall, Limoges, France, 2005-07), p. 496 ; UNSTUDIO (Hotel Castell, Zuoz, Switzerland, 2001-04 / Theater Agora, Lelystad, The Netherlands, 2004-07), p. 502 ; URBANIS (Dafen Art Museum, Dafen Village, Shenzhen, China, 2006-08), p. 516 ; PEKKA VAPAAVUORI (KUMUU, Main Building of the Art Museum of Estonia, Tallinn, Estonia, 2003-06), p. 524 ; VARIOUS ARCHITECTS (Make It Right/Pink Project, New Orleans, Louisiana, USA, 2007-), p. 530 ; WANDEL HOEFER LORCH (Ohel Jakob Synagogue, Jewish Center in Munich, St.…”

“…Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track…”