"zlib_category_name:Computers/Algorithms and Data Structures" - Search

upload/newsarch_ebooks/2022/04/11/B08GVJLQP6.epub

Graph Database Modeling with neo4j (UNREADABLE) Kumar, Anant; Singh, Ajit UNKNOWN, 2020

English [en] · EPUB · 9.1MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Dalbegue Baras Sidiropoulos - Compact Image Coding from Multiscale Edges.PDF

Compact Image Coding from Multiscale Edges Dalbegue, Baras, Sidiropoulos

English [en] · PDF · 0.5MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Sharp - An Implementation of Key-Based Digital Signal Steganography.pdf

An Implementation of Key-Based Digital Signal Steganography Sharp

Motivation......Page 1 Stego Keys......Page 2 Embedding Data......Page 3 Using Digital Images......Page 4 Relation to Other Work......Page 5 Histogram Attacks......Page 6 Digression: Sampling Theory......Page 7 Noise Analysis Attack......Page 8 Hide......Page 9 Conclusion......Page 13 References......Page 14

English [en] · PDF · 1.2MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2022/06/29/7 Algorithm Design Paradigms.pdf

7 Algorithm Design Paradigms Sung-Hyuk Cha Cha Academy llc, 2020

The intended readership includes both undergraduate and graduate students majoring in computer science as well as researchers in the computer science area. The book is suitable either as a textbook or as a supplementary book in algorithm courses. Over 400 computational problems are covered with various algorithms to tackle them. Rather than providing students simply with the best known algorithm for a problem, this book presents various algorithms for readers to master various algorithm design paradigms. Beginners in computer science can train their algorithm design skills via trivial algorithms on elementary problem examples. Graduate students can test their abilities to apply the algorithm design paradigms to devise an efficient algorithm for intermediate-level or challenging problems. Key Features includes followings: 1 Dictionary of Computational Problems: A table of over 400 computational problems with more than 1500 algorithms is provided. 2 Indices and Hyperlinks: Algorithms, computational problems, equations, figures, lemmas, properties, tables, and theorems are indexed with unique identification numbers and page numbers in the printed book and hyperlinked in the e-book version. 3 Extensive Figures: Over 435 figures illustrate the algorithms and describe computational problems. 4 Comprehensive Exercises: More than 352 exercises help students to improve their algorithm design and analysis skills. The answers for most questions are available in the accompanying solution manual.

English [en] · PDF · 15.9MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/upload/zlib · Save

Your ad here.

upload/newsarch_ebooks/2019/12/12/B07DWLBGY1.mobi

C++ Data Structures with Templates: from first principles in C Michael Griffiths Amazon Digital Services, 2018

The book explores the development of a series of key data structures using C and C++. The reader is taken on a graduated journey with each chapter leading to the development of a new C++ template class. The data structures are: A Linked List, including a double linked list that allows a look at list reversal and sorting. A FiFo Queue class based upon a linked list looking at the benefits and subtle challenges of sub-classing. A LiFo Stack class. An introduction to binary trees that includes tree balancing techniques including the DSW algorithm which is a nice introduction to rotations. An AVL self-balancing binary tree that maintains a near optimal balance through insertions and deletions. A Red-Black tree that is optimised for frequent insertions and deletions. A Splay tree optimised for repeated and frequent interaction with a sub-set of the tree data items. A Map class based upon a Red-Black binary tree. Hash tables – developing both the linear probing and separate chaining types. A Vector class representing an efficient resizable array. An introduction to binary heaps and the development of a priority queue based upon a heap. A set class based upon a modified AVL tree. The reader is presented with a range of iterator types and is introduced to template specialization. The book includes C++ demonstration code for each data structure. Additional code samples are supplied for programmers targeting the Arduino IDE which has some variations.

English [en] · MOBI · 0.9MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

upload/newsarch_ebooks/2023/07/19/Graph Algorithms for Data Science MEAP V08.epub

Graph Algorithms for Data Science MEAP V08 Tomaz Bratanic Manning Publications, 2023

Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. 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. 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. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You’ll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you’ll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. about the reader For data scientists who know the basics of Machine Learning. Examples use the Cypher query language, which is explained in the book. about the author Tomaz Bratanic is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations.

English [en] · EPUB · 11.0MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

lgli/D:\!Genesis\!!ForLG\!!!3\Mehlhorn K., Sanders P. Concise algorithmics, the basic toolbox (draft book, CUP)(124s)_CsAl_.pdf

Concise algorithmics, the basic toolbox Mehlhorn K., Sanders P. Cambridge University Press

English [en] · PDF · 1.2MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/F:\twirpx\_19\_9\1978531\1adobe_creative_team_adobe_photoshop_lightroom_5_classroom_in.epub

Adobe Creative Team. Adobe Photoshop Lightroom 5: Classroom in a Book

Adobe Press, 2013. — 416 p. Serious digital photographers, amateur or pro, who seek the fastest, easiest, most comprehensive way to learn Adobe Photoshop Lightroom 5 choose Adobe Photoshop Lightroom 5 Classroom in a Book from the Adobe Creative Team at Adobe Press. The 11 project-based lessons in this book show readers step-by-step the key techniques for working in Photoshop Lightroom 5. And brand-new to this edition is a showcase of extraordinary images by working professional photographers that provides the perfect inspiration. Photoshop Lightroom 5 delivers a complete workflow solution for the digital photographer, from powerful one-click adjustments to a full range of cutting-edge advanced controls. Readers learn how to manage large volumes of digital photographs, work in a non-destructive environment to allow for fearless experimentation, and perform sophisticated image processing tasks to easily produce good-looking pictures and polished presentations for both web and print. This completely revised Photoshop Lightroom 5 edition explains how to fix tilted images and unwanted flaws in one step, and how to create off-center and multiple vignettes within a single image. Learn how to utilize new Smart Previews so you can work on images without bringing your entire library with you. You’ll also learn how to combine still images, video clips, and music into video slide shows that can be viewed on almost any device.

English [en] · EPUB · 29.8MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Guelvouit Pateux - Wide spread spectrum watermarking with side information and interference cancellation.pdf

Wide spread spectrum watermarking with side information and interference cancellation Guelvouit Pateux

English [en] · PDF · 0.7MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

nexusstc/Next-Gen Algorithmic Trading: Strategies, Tools, and Techniques for Professionals with Python/d124d552ba53479045864557a1f8a22b.epub

Next-Gen Algorithmic Trading: Strategies, Tools, and Techniques for Professionals with Python Bisette, Vincent & Van Der Post, Hayden Reactive Publishing, 2024

Absolutely, let's draft a compelling synopsis that reflects the cutting-edge nature of your book Unlock the Future of Finance with "Next-Gen Algorithmic Trading" In a world where milliseconds can mean millions, "Next-Gen Algorithmic Trading" by Hayden Van Der Post is your essential guide to mastering the art and science of modern financial technology. With an expert blend of theory and practice, this book offers an unprecedented look into the strategies, tools, and techniques that are shaping the future of trading. Dive deep into the heart of algorithmic trading with intuitive explanations, Python code samples, and real-world scenarios that bring complex concepts to life. Whether you're a seasoned professional looking to stay ahead of the curve or a newcomer eager to make your mark, this book is designed to elevate your trading strategy to the forefront of the industry. Strategies: Learn the latest algorithmic trading strategies that are setting the markets ablaze. From predictive analytics to machine learning models, gain insights that could redefine your approach to trading. Tools: Get hands-on with the tools that are driving the algo-trading revolution. Discover how to leverage platforms and software that can give you the edge in a hyper-competitive environment. Techniques: Master techniques that harness the power of data and automation. Step-by-step guides and Python scripts provide the knowledge to craft, test, and deploy algorithms that could potentially outperform the market. Hayden Van Der Post brings a wealth of experience to the table, distilling complex information into practical knowledge you can apply immediately. "Next-Gen Algorithmic Trading" is more than just a book—it's a roadmap to success in the digital age of finance. Step into the future with confidence. Step up your trading game. Step into "Next-Gen Algorithmic Trading".

English [en] · EPUB · 1.6MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Data Herding: The art of EDI/ea74afea66dce5d686b3267822292116.epub

Data Herding: The art of EDI Keith Wood Keith Wood, 2022

Do you have to exchange data with your customers? Is your phone constantly ringing (or being emailed or text-ed) asking about files that should have been sent or received but are nowhere to be found?There are common pitfalls whenever files are exchanged between systems. Either with your customer’s external system or between different systems within the same company. There are also common patterns that can be followed to avoid these problems. It is very possible to create a self-monitoring system that will “just work”, and let you know about issues before your trading partner or your internal business unit even knows anything is amiss. This book can help you with data exchanges rather you are using an expensive third-party product, or a series of shell scripts that have been hacked together over time.Keith Wood has been exchanging data between systems for over eighteen years. He has made all of the mistakes and paid the tolls. If you follow the advice given here it can help you avoid going down some of the same bad roads.

English [en] · EPUB · 1.5MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Sojka - A New Algorithm for Direct Corner Detection in Digital Images.rar

A New Algorithm for Direct Corner Detection in Digital Images Sojka

English [en] · RAR · 1.7MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2023/07/02/Graph Algorithms for Data Science.epub

Graph Algorithms for Data Science (MEAP v7) Tomaž Bratanič Manning Publications, Chaptes 1 to 11 of 12, 2023

Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. 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. 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. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You’ll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you’ll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. about the reader For data scientists who know the basics of machine learning. Examples use the Cypher query language, which is explained in the book. about the author Tomaž Bratanič is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations.

English [en] · EPUB · 10.0MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

nexusstc/Parallel Iterative Algorithms. From Sequential to Grid Computing/305d0c1343f7fc9b8640fd33bf6c48d3.pdf

Parallel Iterative Algorithms. From Sequential to Grid Computing Jacques Mohcine Bahi, Sylvain Contassot-Vivier, Raphaël Couturier Chapman & Hall/CRC, Numerical Analysis and Scientific Computing, 2008

Parallel Iterative Algorithms: From Sequential to Grid Computing Contents List of Tables List of Figures Acknowledgments Appendix References Introduction Appendix References Chapter 1: Iterative Algorithms 1.1 Basic theory 1.1.1 Characteristic elements of a matrix 1.1.2 Norms 1.2 Sequential iterative algorithms 1.3 A classical illustration example Appendix References Chapter 2: Iterative Algorithms and Applications to Numerical Problems Introduction 2.1 Systems of linear equations 2.1.1 Construction and convergence of linear iterative algorithms 2.1.2 Speed of convergence of linear iterative algorithms 2.1.3 Jacobi algorithm 2.1.4 Gauss-Seidel algorithm 2.1.5 Successive overrelaxation method 2.1.6 Block versions of the previous algorithms 2.1.7 Block tridiagonal matrices 2.1.8 Minimization algorithms to solve linear systems 2.1.8.1 Descent and Gradient algorithms 2.1.8.2 Conjugate gradient algorithm 2.1.8.3 GMRES 2.1.8.4 BiConjugate Gradient algorithm 2.1.9 Preconditioning 2.1.9.1 Jacobi, SOR, SSOR and ILU preconditioning 2.1.9.2 Preconditioning matrices for the conjugate gradient algorithm 2.1.9.3 Implementation of the preconditioned conjugate gradient solver 2.1.9.4 Incomplete LU factorization 2.2 Nonlinear equation systems 2.2.1 Derivatives 2.2.2 Newton method 2.2.3 Convergence of the Newton method 2.3 Exercises Appendix References Chapter 3: Parallel Architectures and Iterative Algorithms Introduction 3.1 Historical context 3.2 Parallel architectures 3.2.1 Classifications of the architectures 3.2.1.1 Parallel machines 3.2.1.2 Local clusters 3.2.1.3 Distributed clusters/grids 3.3 Trends of used configurations 3.4 Classification of parallel iterative algorithms 3.4.1 Synchronous iterations - synchronous communications (SISC) 3.4.2 Synchronous iterations - asynchronous communications (SISC) 3.4.3 Asynchronous iterations - asynchronous communications (AIAC) 3.4.3.1 Semi-flexible communications 3.4.3.2 Flexible communications 3.4.4 What PIA on what architecture? 3.4.4.1 Parallel machines 3.4.4.2 Local clusters 3.4.4.3 Distributed clusters/grids Appendix References Chapter 4: Synchronous Iterations Introduction 4.1 Parallel linear iterative algorithms for linear systems 4.1.1 Block Jacobi and O’Leary and White multisplitting algorithms 4.1.2 General multisplitting algorithms 4.1.2.1 Obtaining O’Leary and White multisplitting 4.1.2.2 Obtaining discrete analogues of Schwarz alternating algorithms 4.1.2.3 Obtaining discrete analogues of multisubdomain Schwarz algorithms 4.1.2.4 Convergence of multisplitting and two-stage multisplitting algorithms 4.2 Nonlinear systems: parallel synchronous Newton-multisplitting algorithms 4.2.1 Newton-Jacobi algorithms 4.2.2 Newton-multisplitting algorithms 4.3 Preconditioning 4.4 Implementation 4.4.1 Survey of synchronous algorithms with shared memory architecture 4.4.2 Synchronous Jacobi algorithm 4.4.3 Synchronous conjugate gradient algorithm 4.4.4 Synchronous block Jacobi algorithm 4.4.5 Synchronous multisplitting algorithm for solving linear systems 4.4.5.1 Overlapping strategy that uses locally computed values 4.4.5.2 Overlapping strategy that uses values computed by close neighbors 4.4.5.3 Overlapping strategy that mixes overlapped components with close neighbors 4.4.5.4 Overlapping strategy that mixes all overlapped components 4.4.6 Synchronous Newton-multisplitting algorithm 4.5 Convergence detection 4.6 Exercises Appendix References Chapter 5: Asynchronous Iterations Introduction 5.1 Advantages of asynchronous algorithms 5.2 Mathematical model and convergence results 5.2.1 The mathematical model of asynchronous algorithms 5.2.2 Some derived basic algorithms 5.2.3 Convergence results of asynchronous algorithms 5.3 Convergence situations 5.3.1 The linear framework 5.3.2 The nonlinear framework 5.4 Parallel asynchronous multisplitting algorithms 5.4.1 A general framework of asynchronous multisplitting methods 5.4.2 Asynchronous multisplitting algorithms for linear problems 5.4.3 Asynchronous multisplitting algorithms for nonlinear problems 5.4.3.1 Extended fixed point mapping associated with... 5.4.3.2 The discrete analogue of Schwarz alternating method and its multisubdomain generalizations 5.4.3.3 Discrete analogue of the Schwarz alternating method 5.4.3.4 Discrete analogue of the multisubdomain Schwarz method 5.5 Coupling Newton and multisplitting algorithms 5.5.1 Newton-multisplitting algorithms: multisplitting algorithms as inner algorithms in the Newton method 5.5.2 Nonlinear multisplitting-Newton algorithms 5.6 Implementation 5.6.1 Some solutions to manage the communications using threads 5.6.2 Asynchronous Jacobi algorithm 5.6.3 Asynchronous block Jacobi algorithm 5.6.4 Asynchronous multisplitting algorithm for solving linear systems 5.6.5 Asynchronous Newton-multisplitting algorithm 5.6.6 Asynchronous multisplitting-Newton algorithm 5.7 Convergence detection 5.7.1 Decentralized convergence detection algorithm 5.7.1.1 Local convergence detection 5.7.1.2 Global convergence detection 5.8 Exercises Appendix References Chapter 6: Programming Environments and Experimental Results Introduction 6.1 Implementation of AIAC algorithms with non-dedicated environments 6.1.1 Comparison of the environments 6.1.1.1 Performances 6.1.1.2 Ease of programming 6.1.1.3 Ease of deployment 6.2 Two environments dedicated to asynchronous iterative algorithms 6.2.1 JACE 6.2.1.1 The daemon 6.2.1.2 The computing task 6.2.1.3 The spawner 6.2.2 CRAC 6.2.2.1 Architecture 6.3 Ratio between computation time and communication time 6.4 Experiments in the context of linear systems 6.4.1 Context of experimentation 6.4.2 Comparison of local and distant executions 6.4.3 Impact of the computation amount 6.4.4 Larger experiments 6.4.5 Other experiments in the context of linear systems 6.4.5.1 Influence of the overlapping 6.4.5.2 Memory requirements with a direct method 6.5 Experiments in the context of partial differential equations using a finite difference scheme Appendix References Appendix A-1 Diagonal dominance. Irreducible matrices A-1.1 Z-matrices, M -matrices and H-matrices A-1.2 Perron-Frobenius theorem A-1.3 Sequences and sets References References Appendix

English [en] · PDF · 2.5MB · 2008 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

Cyberboss: The Rise of Algorithmic Management and the New Struggle for Control at Work Craig Gent Verso Books, S.l, 2024

How technologies of organization are redrawing the lines of class struggle Across the world, algorithms are changing the nature of work. Nowhere is this clearer than in the logistics and distribution sectors, where workers are instructed, tracked and monitored by increasingly dystopian management technologies. In Cyberboss, Craig Gent takes us into workplaces where algorithms rule to excavate the politics behind the newest form of managerial power. Combining worker testimony and original research on companies such as Amazon, Uber, and Deliveroo, the cutting edge of algorithmic management technology, this book reveals the sometimes unexpected effects these new techniques have on work, workers and managers. Gent advances an alternative politics of resistance in the face of digital control.

English [en] · PDF · 1.8MB · 2024 · 📘 Book (non-fiction) · 🚀/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark\Bors Pitas - Image watermarking using block site selection and DCT domain constraints.pdf

Image watermarking using block site selection and DCT domain constraints Bors, Pitas

English [en] · PDF · 0.6MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Lee Chen - Object-based image steganography using affine transformation.PDF

Object-based image steganography using affine transformation Lee Chen

English [en] · PDF · 0.9MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Stern Tillich - Automatic Detection of a Watermarked Document Using a Private Key.pdf

Automatic Detection of a Watermarked Document Using a Private Key Stern Tillich

Introduction......Page 1 Related Work......Page 3 Algorithm......Page 4 Adversarial Model and Assumptions......Page 6 Preliminaries for the Proof of Theorem......Page 7 Analysis in Presence of Marked Data......Page 8 Discussion......Page 9 Experimental Results......Page 11 Conclusion......Page 13

English [en] · PDF · 0.2MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/F:\twirpx\_18\_8\1802176\barnett_granville_del_tongo_luca_data_structures_and_algorit.pdf

Data Structures and Algorithms: Annotated Reference with Examples Barnett Granville, Del Tongo Luca. 2008

Dotnetslackers.com, 2008. — 112 p. This book written by Granville Barnett and Luca Del Tongo is part of an effort to provide all developers with a core understanding of algorithms that operate on various common, and uncommon data structures. Contents Introduction. What this book is, and what it isn’t. Assumed knowledge. Big Oh notation. Imperative programming language. Object oriented concepts. Pseudocode. Tips for working through the examples. Book outline. Testing. Where can I get the code? Final messages. Data Structures. Linked Lists. Singly Linked List. Insertion. Searching. Deletion. Traversing the list. Traversing the list in reverse order. Doubly Linked List. Insertion. Deletion. Reverse Traversal. Summary. Binary Search Tree. Insertion. Searching. Deletion. Finding the parent of a given node. Attaining a reference to a node. Finding the smallest and largest values in the binary search tree. Tree Traversals. Preorder. Postorder. Inorder. Breadth First. Summary. Heap. Insertion. Deletion. Searching. Traversal. Summary. Sets. Unordered. Insertion. Ordered. Summary. Queues. A standard queue. Priority Queue. Double Ended Queue. Summary. AVL Tree. Tree Rotations. Tree Rebalancing. Insertion. Deletion. Summary. Algorithms. Sorting. Bubble Sort. Merge Sort. Quick Sort. Insertion Sort. Shell Sort. Radix Sort. Summary. Numeric. Primality Test. Base conversions. Attaining the greatest common denominator of two numbers. Computing the maximum value for a number of a specific base consisting of N digits. Factorial of a number. Summary. Searching. Sequential Search. Probability Search. Summary. Strings. Reversing the order of words in a sentence. Detecting a palindrome . . . . . . Counting the number of words in a string. Determining the number of repeated words within a string. Determining the first matching character between two strings. Summary. Algorithm Walkthrough. Iterative algorithms. Recursive Algorithms. Summary. Translation Walkthrough. Summary. Recursive Vs. Iterative Solutions. Activation Records. Some problems are recursive in nature. Summary. Testing. What constitutes a unit test? When should I write my tests? How seriously should I view my test suite? The three A’s. The structuring of tests. Code Coverage. Summary. Symbol Definitions.

English [en] · PDF · 1.1MB · 2008 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

lgli/M_Mathematics/MT_Number theory/MTc_Computational number theory/Arun-Kumar S. Algorithmic number theory(web draft, 2002)(200s)_MTc_.pdf

Algorithmic number theory Arun-Kumar S. web draft, 2002

English [en] · PDF · 0.7MB · 2002 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/John Farrier - Data Structures and Algorithms with the C++ STL.epub.sanet.st.epub

Data Structures and Algorithms with the C++ STL: A guide for modern C++ practitioners JOHN. FARRIER Packt Publishing, Limited, 1, 2024

Explore the C++ STL with practical guidance on vectors, algorithms, and custom types for intermediate developers, enriched by real-world examples. Key Features: Master the std:: vector and understand why it should be your default container of choice Understand each STL algorithm and its practical applications Gain insights into advanced topics such as exception guarantees and thread safety Purchase of the print or Kindle book includes a free PDF eBook Book Description: While the Standard Template Library (STL) offers a rich set of tools for data structures and algorithms, navigating its intricacies can be daunting for intermediate C++ developers without expert guidance. This book offers a thorough exploration of the STL's components, covering fundamental data structures, advanced algorithms, and concurrency features. Starting with an in-depth analysis of the std:: vector, this book highlights its pivotal role in the STL, progressing toward building your proficiency in utilizing vectors, managing memory, and leveraging iterators. The book then advances to STL's data structures, including sequence containers, associative containers, and unordered containers, simplifying the concepts of container adaptors and views to enhance your knowledge of modern STL programming. Shifting the focus to STL algorithms, you'll get to grips with sorting, searching, and transformations and develop the skills to implement and modify algorithms with best practices. Advanced sections cover extending the STL with custom types and algorithms, as well as concurrency features, exception safety, and parallel algorithms. By the end of this book, you'll have transformed into a proficient STL practitioner ready to tackle real-world challenges and build efficient and scalable C++ applications. What You Will Learn: Streamline data handling using the std:: vector Master advanced usage of STL iterators Optimize memory in STL containers Implement custom STL allocators Apply sorting and searching with STL algorithms Craft STL-compatible custom types Manage concurrency and ensure thread safety in Harness the power of parallel algorithms in STL Who this book is for: This book is for intermediate-level C++ developers looking to enhance their software development skills. Familiarity with basic C++ syntax and object-oriented programming (OOP) as well as some exposure to data structures and algorithms is assumed. Tailored to software engineers, computer science students, and hobbyist programmers, this book delves into C++ STL for practical application, performance enhancement, and efficient coding prac

English [en] · EPUB · 5.1MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save

lgli/Ultimate Python Libraries for Data Analysis and Visualization (Abhinaba Banerjee).pdf

Ultimate Python Libraries for Data Analysis and Visualization Abhinaba Banerjee Orange Education Pvt Ltd, AVATM, 2024

Test your Data Analysis skills to its fullest using Python and other no-code tools. Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets.Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more.By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax.Table of Contents1. Introduction to Data Analysis and Data Visualization using Python2. Data Acquisition3. Data Cleaning and Preparation4. Exploratory Data Analysis5. Statistical Analysis6. Time Series Analysis and Forecasting7. Signal Processing8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index

English [en] · PDF · 9.0MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save

lgli/P:\BooksCollection\!2\Preiss B.R.Data structures and algorithms with object-oriented design patterns in C++.1997.chm

Data structures and algorithms with object-oriented design patterns in C++ Preiss B.R. 1997

English [en] · CHM · 3.4MB · 1997 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Topics in Theoretical Computer Science: An Algorithmist's Toolkit: Lecture Notes/e2c31d80c0bce35a35240d7ac32601ef.pdf

Topics in Theoretical Computer Science: An Algorithmist's Toolkit: Lecture Notes Prof. Jonathan Kelner Massachusetts Institute of Technology (MIT), 2009

English [en] · PDF · 5.9MB · 2009 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

nexusstc/Analytics in a Big Data World. The Essential Guide to Data Science and its Applications/fb152a1838f5f1f4dcc449ffbd4eeefa.pdf

Analytics in a Big Data World. The Essential Guide to Data Science and its Applications Bart Baesens Wiley, 2014

The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the authors personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

English [en] · PDF · 8.1MB · 2014 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark\Wang - A wavelet-based watermarking algorithm for ownership verification of digital images.pdf

A wavelet-based watermarking algorithm for ownership verification of digital images Wang

English [en] · PDF · 2.3MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/dvd52/Alpay D., Vinnikov V. (Ed) - System Theory, the Schur Algorithm and Multidimensional Analysis(2007)(322).pdf

System Theory, the Schur Algorithm and Multidimensional Analysis Alpay D., Vinnikov V. (Ed) 2007

This volume contains six peer-refereed articles written on the occasion of the workshop Operator theory, system theory and scattering theory: multidimensional generalizations and related topics, held at the Department of Mathematics of the Ben-Gurion University of the Negev during the period June 26-July 1, 2005. The papers present the newest developments in key directions of current research in complex analysis and operator theory. Topics considered include Schur analysis, hierarchical semiseparable matrices, canonical forms for pairs of quaternionic matrices, the theory of homogeneous operators, algebras of fractions of continuous functions, and moment problems. Schur analysis in its various aspects occupies more than half of the volume, and moments problems have also an important place in the papers presented here.The volume will be of interest to a wide audience of pure and applied mathematicians, electrical engineers and theoretical physicists.

English [en] · PDF · 3.5MB · 2007 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Algorithms for Sequential Decision Making/07d5d05021248c05a4074543e305c09c.pdf

Algorithms for Sequential Decision Making Michael Lederman Littman Brown University, 1996

English [en] · PDF · 1.3MB · 1996 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/W\Witley Darrel\Math - A Genetic Algorithm Tutorial Pdf.PDF

Math - A Genetic Algorithm Tutorial Witley, Darrel 0

English [en] · Spanish [es] · PDF · 0.4MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Fletcher Larkin - Direct Embedding and Detection of RST Invariant Watermarks.pdf

Direct Embedding and Detection of RST Invariant Watermarks Fletcher Larkin

Introduction......Page 1 2-D Fourier Mellin Basis Function......Page 2 Homogeneous Functions......Page 3 Orthogonality of Truncated LRHFs......Page 6 The Remarkable Spectral Properties of LRHFs......Page 7 Optimal Detection: Correlation and Translation Invariance......Page 8 Embedding Real Marks and Detecting with Complex Patterns......Page 10 Detection Algorithm......Page 11 Performance: Resistance to Watermark Attacks......Page 14 References......Page 15

English [en] · PDF · 1.2MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/F:\twirpx\_17\_7\1572108\yankov_k_tolekova_a_influence_of_captopril_treatment_of_plas.pdf

Influence of Captopril Treatment of Plasma Renin Activity - Mathematical Model Yankov K., Tolekova A.

Notulae Scientia Biologicae. vol.2,(3), 2010,Romania, pp.7- 11. Print ISSN 2067-3205, Electronic ISSN 2067-3264 Abstract. A model of the dynamics of plasma renin activity under the influence of various doses of captopril is formulated. The influence of captopril on renin angiotensin system is different from the effects of the other studied drugs – nifedipine and nicardipine. Captopril inhibits the feedback in renin-angiotensin system and the upward trend of the renin activity is a proportional of the intrinsic growth rate. This dependence can be described using a modified Verhulst logistic function is proposed. The model is identified using the Korelia-Dynamics program. As optimization method for data identification a cyclic coordinate descent method is used. The residuals between the experimental data and the identified model are minimized applying least square or uniform fitting. The model allows prediction the effects of different captopril doses and permits the researcher to study the behavior of the renin angiotensin system under variety of conceivable conditions.

English [en] · PDF · 0.5MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2023/10/03/machine_learning_in_python_for_dynamic.pdf

Machine Learning in Python for Dynamic Process Systems Ankur Kumar, Jesus Flores-Cerrillo Leanpub, ML for Process Industry Series, 2023

This book provides a comprehensive coverage of Machine Learning (ML) methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show (using Python) how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods. This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning for dynamic process modeling. Applications on time series analysis, process disturbance modeling, system identification, and process fault detection are illustrated with examples. Upon completion, readers will be able to confidently navigate the system identification literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into three parts. Part 1 of the book provides perspectives on the importance of ML for dynamic process modeling and lays down the basic foundations of ML-DPM (machine learning for dynamic process modeling). Part 2 provides in-detail presentation of classical ML techniques (such as ARX, FIR, OE, ARMAX, ARIMAX, CVA, NARX, etc.) and has been written keeping in mind the different modeling requirements and process characteristics that determine a model’s suitability for a problem at hand. These include, amongst others, presence of multiple correlated outputs, process nonlinearity, need for low model bias, need to model disturbance signal accurately, etc. Part 3 is focused on artificial neural networks and deep learning. The following topics are broadly covered: · Exploratory analysis of dynamic dataset · Best practices for dynamic modeling · Linear and discrete-time classical parametric and non-parametric models · State-space models for MIMO systems · Nonlinear system identification and closed-loop identification · Neural networks-based dynamic process modeling Who should read this book: The application-oriented approach in this book is meant to give a quick and comprehensive coverage of dynamic modeling methodologies in a coherent, reader-friendly, and easy-to-understand manner. The following categories of readers will find the book useful: 1) Data scientists new to the field of system identification 2) Regular users of commercial process modeling software looking to obtain a deeper understanding of the underlying concepts 3) Practicing process data scientists looking for guidance for developing process modeling and monitoring solutions for dynamic systems 4) Process engineers or process engineering students making their entry into the world of Data Science Pre-requisites: No prior experience with Machine Learning or Python is needed. Undergraduate-level knowledge of basic linear algebra and calculus is assumed.

English [en] · PDF · 10.7MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

nexusstc/Physically Based Rendering. From Theory to Implementation/826bbd9792f9bcb588263d04ab4f68f9.djvu

Physically Based Rendering. From Theory to Implementation Matt Pharr, Greg Humphreys Morgan Kaufmann Publishers, Morgan Kaufmann series in computer graphics and geometric modeling, 2nd, 2010

English [en] · DJVU · 11.6MB · 2010 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Dumitrescu Wu Wang - Detection of LSB Steganography via Sample Pair Analysis.PDF

Detection of LSB Steganography via Sample Pair Analysis Dumitrescu Wu Wang

Introduction......Page 1 Trace Multisets of Sample Pairs......Page 2 Detection of LSB Steganography......Page 4 Accuracy of Estimated Hidden Message Length......Page 6 Possible Attacks and Counter Measures......Page 11 Conclusion......Page 14

English [en] · PDF · 0.4MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Nagai Ikehara Kaneko Kurematsu - Generalized Unequal Length Lapped Orthogonal Transform for Subband Image Coding.pdf

Generalized Unequal Length Lapped Orthogonal Transform for Subband Image Coding Nagai, Ikehara, Kaneko, Kurematsu

English [en] · PDF · 0.9MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1541894-Новая подборка книг по цифровой обработке сигналов, распознава\Pattern recognition in speech and language processing.rar

Pattern recognition in speech and language processing WU CHOU, BIING HWANG JUANG CRC Press, 2003

English [en] · RAR · 3.6MB · 2003 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Venkatesan Kesal - Cryptanalysis of Discrete-Sequence Spread Spectrum Watermarks.pdf

Cryptanalysis of Discrete-Sequence Spread Spectrum Watermarks Venkatesan Kesal

Introduction......Page 1 Contribution of Our Work:......Page 2 Notation:......Page 3 Source Model......Page 4 On dsss Watermarking Methods......Page 5 Key Extraction......Page 6 Estimation Analysis - Most General Case......Page 7 Estimation Analysis - Block Repetition Code......Page 8 Distortion Induced by Proposed Attack......Page 10 Single Random Variable Case:......Page 11 Block Repetition Code Case......Page 12 Simulation Results Based on Quantitative Analysis of the Attack......Page 13 Practical Details and Experimental Results of Proposed Attack Methods on Audio Clips......Page 14 Proof of Lemma 2......Page 17 Proof of Lemma 4......Page 18 Proof of Lemma 5......Page 19

English [en] · PDF · 0.3MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Cs_Computer science/CsIp_Image processing/Morse B.S. Lectures on image processing (no lecture 1, 8, 19)(free web version, 2000)(92s)_CsIp_.pdf

Lectures on image processing Morse B.S. free web version, 2000

Lecture 2. Image processing: Connectivity......Page 1 Lecture 3. Data structures......Page 8 Lecture 4. Thresholding......Page 11 Lecture 5. Binary morphology......Page 16 Lecture 6. Morphology, cont'd......Page 22 Lecture 7. Shape description (contours)......Page 25 Lecture 9. Shape description (regions)......Page 33 Lecture 10. Shape description (regions, cont'd)......Page 39 Lecture 11. Differential geometry......Page 44 Lecture 12. Local image preprocessing (smoothing)......Page 51 Lecture 13. Edge detection......Page 53 Lecture 14. Edge detection, cont'd......Page 59 Lecture 15. Edge-based segmentation. Hough transform......Page 62 Lecture 16. Edge-based image segmentation......Page 67 Lecture 17. Segmentation, cont'd......Page 70 Lecture 18. Region-based segmentation......Page 74 Lecture 20. Segmentation (matching, advanced)......Page 80 Lecture 21. Image understanding......Page 83 Lecture 22. Texture......Page 89

English [en] · PDF · 0.4MB · 2000 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/DVD-027/Fulton_J.,_Fulton_S.M._Adobe_Photoshop_Elements_3_in_a_Snap_(2004)(en)(768s).chm

Adobe Photoshop Elements 3 in a Snap Fulton J., Fulton S.M. 2004

These days, nobody really wants to learn everything there is about a software product like Photoshop Elements. And even if you did, who has the time to endlessly tinker and play with it until you figure everything out?You just want a book that will quickly show you how to do things with Photoshop Elements - things that aren't already covered in the docs, and things you can't just figure out on your own.Adobe Photoshop Elements in a Snap is designed specifically for today's computer user. * Somebody who is new to Photoshop Elements, but not new to computers. * Somebody who doesn't have time for long-winded, mind-numbing explanations - and certainly no time or patience for bad jokes.Comprised of a series of well-organized, bite-sized, quickly accomplished tasks, the book lets the reader zero right in on the one particular task he or she wants to accomplish, quickly figure out what to do, do it, and then get back to work.

English [en] · CHM · 33.3MB · 2004 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

lgli/F:\twirpx\_11\_1\563261\1boisvert_r_pozo_r_remington_k_the_matrix_market_exchange_for.pdf

The Matrix Market Exchange Formats: Initial Design Ronald F. Boisvert; Roldan Pozo; Karin A. Remington National Institute of Standards and Technology, NISTIR, 5935, 1996

We propose elementary ASCII exchange formats for matrices. Specific instances of the format are defined for dense and sparse matrices with real, complex, integer and pattern entries, with special cases for symmetric, skew-symmetric and Hermitian matrices. Sparse matrices are represented in a coordinate storage format. The overall file structure is designed to allow future definition of other specialized matrix formats, as well as for objects other than matrices. Introduction Coordinate Format for Sparse Matrices Array Format for Dense Matrices Specification of the Base MM File Format Specification of MM Formats for Matrices Coordinate Formats for Sparse Matrices Array Formats for Dense Matrices Extending the Base MM Formats Structured Comments Format Specializations Extending the MM Type Code

English [en] · PDF · 0.2MB · 1996 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Fridricha Goljanb Memonc Shended Wonge - On the security of the Yeung-Mintzer authentication watermark.pdf

Memonc Shended Wonge - On the security of the Yeung-Mintzer authentication watermark Fridricha Goljanb

English [en] · PDF · 0.2MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/ISO/IEC 18181-1 Information technology — JPEG XL Image Coding System — Part 1: Core coding system/193af952ee2cb4c85105e382921932b9.pdf

ISO/IEC 18181-1 Information technology — JPEG XL Image Coding System — Part 1: Core coding system ISO Chuo Kouron Shinsha Co., Ltd., 1, FDIS, 2021

Defines the JPEG XL codestream and decoder, which can be used for lossy encoding, lossless encoding, and lossless recompression of existing JPEG images.

English [en] · PDF · 1.7MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2022/12/29/extracted__Feature_Engineering__Selection_for_Explainable_Models__A_Second_Course_for_Data_Scientists.zip/Feature Engineering & Selection for Explainable Models A Second Course for Data Scientists/Feature Engineering & Selection for Explainable Models A Second Course for Data Scientists.pdf

Feature Engineering & Selection for Explainable Models Md Azimul Haque Leanpub, 2023

I found the root cause of many challenges faced by my students who recently transitioned into data science and machine learning. I have tried to address these issues in my book and would like to dedicate this book to all my students for all the love and respect I have received.

English [en] · PDF · 6.3MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

Grokking Algorithms, 2nd Edition Aditya Y. Bhargava Manning Publications Co. LLC, 2, 2nd, US, 2024

A friendly, fully-illustrated introduction to the most important computer programming algorithms. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. This book will prepare you for those pesky algorithms questions in every programming job interview and help you apply them in your day-to-day work. And if you want to understand them without slogging through dense multipage proofs, this is the book for you. In Grokking Algorithms, Second Edition you will discover: Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP complete and greedy algorithms Performance trade-offs between algorithms Exercises and code samples in every chapter Over 400 illustrations with detailed walkthroughs The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn't have to be complicated or boring! This new edition now includes fresh coverage of trees, NP complete problems, and code updates to Python 3. With easy-to-read, friendly explanations, clever examples, and exercises to sharpen your skills as you learn, you’ll actually enjoy learning these important algorithms. About the book Grokking Algorithms, Second Edition makes it easy to learn. You’ll never be bored—complex concepts are all explained through fun cartoons and memorable examples that make them stick. You'll start with tasks like sorting and searching, then build your skills to tackle more advanced problems like data compression and artificial intelligence. This revised second edition contains brand new coverage of trees, including binary search trees, balanced trees, B-trees and more. You’ll also discover fresh insights on data structure performance that takes account of modern CPUs. Plus, the book’s fully annotated code samples have been updated to Python 3. By the time you reach the last page, you’ll have mastered the most widely applicable algorithms, know when and how to use them, and be fully prepared when you’re asked about them on your next job interview. About the reader Suitable for self-taught programmers, engineers, job seekers, or anyone who wants to brush up on algorithms. About the author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io.

English [en] · EPUB · 23.3MB · 2024 · 📘 Book (non-fiction) · 🚀/zlib · Save

Your ad here.

nexusstc/A Meliorated Kashida Based Approach for Arabic Text Steganography/3ae41b44d36ac2fc688b5e1015388dbf.pdf

A Meliorated Kashida Based Approach for Arabic Text Steganography Ala’a M. Alhusban, Jehad Q. Odeh Alnihoud International Journal of Computer Science & Information Technology (IJCSIT), International Journal of Computer Science and Information Technology, #2, 9, pages 99-112, 2017 apr 30

A new method to hide secret message within Arabic texts has been proposed. This method depends primarily on the nature of letters; whether they are pointed or un-pointed letters. We have exploited this feature of Arabic text to add a kashida (-). Since there are two cases of each letter; pointed or un-pointed, a table of four cases is used to add a kashida between two letters every time to hide two bits in each kashida. The most common kashida-based methods hided just one bit in each kashida or used a kashida as well as a zero-width character to hide two bits, while the proposed method hided two bits using kashida without the need to add the zero-width character. Adding zero-width character increases the file size dramatically, which affects the security measures. By hiding two bits in each kashida, the capacity is remarkably increased as compared with some of the well-known kashida-based approaches. The dependency on the nature of both of the surrounding letters of the kashida as well as dividing the cover text into two blocks, each one is being dealt with in a different way, increases the security of the proposed method. Since, intruders are not able to expect the method of extraction. Furthermore, the original kashida case is considered. Ignoring the original kashida in the cover text affects the accuracy of extraction phase. A system of embedding the secret message within a cover text and extracting the secret message from a stego text has been built. This system has a hashing phase in order to distinguish between the authorized user and the un-authorized user, which may increase the robustness of the system. In some cases, the capacity ratio of the proposed method may affected by the sequences of the secret bits, and suitable appearances of the targeted kashida(-). This might be considered as limitation that may yield to undesirable results in such cases. As a future work, we should overcome this drawback by proposing a suitable method to make use of all kashidas irrespective to the sequences of the secret bits.

English [en] · PDF · 0.2MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Deep Learning for Computer Vision with Python/46e78a6af27c998c5a85d8fe5ad2600e.pdf

Deep Learning for Computer Vision with Python 2-Practitioner Bundle Adrian Rosebrock PyImageSearch, 2-Practitioner Bundle, 1.10, 2017

Welcome to the Practitioner Bundle of Deep Learning for Computer Vision with Python! This volume is meant to be the next logical step in your deep learning for computer vision education after completing the Starter Bundle. At this point, you should have a strong understanding of the fundamentals of parameterized learning, neural networks, and Convolutional Neural Networks (CNNs). You should also feel relatively comfortable using the Keras library and the Python programming language to train your own custom deep learning networks. The purpose of the Practitioner Bundle is to build on your knowledge gained from the Starter Bundle and introduce more advanced algorithms, concepts, and tricks of the trade—these techniques will be covered in three distinct parts of the book.

English [en] · PDF · 10.0MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2023/07/02/Elasticsearch in Action, Second Edition MEAP V13.epub

Elasticsearch in Action, Second Edition (MEAP V13) Madhusudhan Konda Manning Publications, 2 / All 15 chapters, 2023

Build powerful, production-ready search applications using the incredible features of Elasticsearch. In ElasticSearch in Action, Second Edition you will discover Architecture, concepts, and fundamentals of Elasticsearch Installing, configuring and running Elasticsearch and Kibana Creating an index with custom settings Data types, mapping fundamentals, and templates Fundamentals of text analysis and working with text analyzers Indexing, deleting, and updating documents Indexing data in bulk and reindexing and aliasing operations Learning search concepts, relevancy scores and similarity algorithms Elasticsearch in Action, Second Edition teaches you to build scalable search applications using Elasticsearch. This completely new edition explores Elasticsearch fundamentals from the ground up. You’ll deep dive into design principles, search architectures, and Elasticsearch’s essential APIs. Every chapter is clearly illustrated with diagrams and hands-on examples. You’ll even explore real-world use cases for full text search, data visualisations, and machine-learning. about the technology Modern search seems like magic. You type a few words and the search engine appears to know what you want. With the Elasticsearch near-real-time search and analytics engine, you can give your users this magical experience without having to do complex low-level programming or understand advanced data science algorithms. You just install it, tweak it, and get on with your work. about the book Elasticsearch in Action, Second Edition is a hands-on guide to developing fully functional search engines with Elasticsearch and Kibana. Rewritten for the latest version of Elasticsearch, this completely new second edition explores Elasticsearch’s high-level architecture, reveals infrastructure patterns, and walks through the search and analytics capabilities of numerous Elasticsearch APIs. It covers dozens of awesome techniques, such as Developing a multitude of search queries Working with various query types Enabling search results with sorting and pagination functionality Writing and working with advanced search queries Working analytics and aggregations Developing high level visualizations in Kibana Configuring and scaling the clusters, and tuning performance You’ll quickly progress from the basics of installation and configuring clusters, to indexing documents, advanced aggregations, and putting your servers into production. By the time you’re done, you’ll be ready to build amazing search engines for your clients that take advantage of Elasticsearch’s modern features. about the reader For application developers familiar with DevOps and web services. about the author Madhusudhan Konda is a full-stack lead engineer, architect, mentor, and conference speaker. He delivers live online training on Elasticsearch and the Elastic Stack.

English [en] · EPUB · 23.4MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

lgli/D:\!Genesis\!!ForLG\1991178Цифровые водяные знаки\article\!Watermark2\Miller Doerr Cox - Applying Informed Coding and Embedding to Design a Robust, High Capacity Watermark.pdf

Applying Informed Coding and Embedding to Design a Robust, High Capacity Watermark Miller, Doerr, Cox

English [en] · PDF · 0.5MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Solving the reconstruction-generation trade-off: Generative model with implicit embedding learning/36e981af1e99b8c44edb1cfdc256e731.pdf

Solving the reconstruction-generation trade-off: Generative model with implicit embedding learning (Neurocomputing) Cong Geng, Jia Wang, Li Chen, Zhiyong Gao Elsevier BV, Neurocomputing, 549, 549, 2023

Variational Autoencoder (VAE) and Generative adversarial network (GAN) are two classic generative models that generate realistic data from a predefined prior distribution, such as a Gaussian distribution. One advantage of VAE over GAN is its ability to simultaneously generate high-dimensional data and learn latent representations that are useful for data manipulation. However, it has been observed that a tradeoff exists between reconstruction and generation in VAE, as matching the prior distribution for the latent representations may destroy the geometric structure of the data manifold. To address this issue, we propose an autoencoder-based generative model that allows the prior to learn the embedding distribution, rather than imposing the latent variables to fit the prior. To preserve the geometric structure of the data manifold to the maximum, the embedding distribution is trained using a simple regularized autoencoder architecture. Then an adversarial strategy is employed to achieve a latent mapping. We provide both theoretical and experimental support for the effectiveness of our method, which eliminates the contradiction between preserving the geometric structure of the data manifold and matching the distribution in latent space. The code is available at https://github.com/gengcong940126/GMIEL.

English [en] · PDF · 5.0MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Your ad here.

lgli/F:\twirpx\_18\_8\1780654\peng_roger_d_matsui_elizabeth_the_art_of_data_science_a_guid.pdf

The Art of Data Science: A guide for everyone who works with data Peng Roger D., Matsui Elizabeth. Leanpub, 1, 2015

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

English [en] · PDF · 6.5MB · 2015 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save