Informatik

Gain a fundamental understanding of Python¿s syntax and features with this revised introductory and practical reference. Covering a wide array of Python¿related programming topics, including addressing language internals, database integration, network programming, and web services, yoüll be guided by sound development principles. Updated to reflect the latest in Python programming paradigms and several of the most crucial features found in Python 3, Beginning Python, Fourth Edition also covers advanced topics such as extending Python and packaging/distributing Python applications. Ten accompanying projects will ensure you can get your hands dirty in no time. You will: Become a proficient Python programmer by following along with a friendly, practical guide to the language¿s key features Write code faster by learning how to take advantage of advanced features such as magic methods, exceptions, and abstraction Gain insight into modern Python programming paradigms including testing, documentation, packaging, and distribution Work through several interesting projects, including a P2P file¿sharing application, chat client, video game, remote text editor, and more Who This Book Is For Programmers, novice and otherwise, seeking a comprehensive introduction to the Python programming language.

Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2024 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz.

This book introduces the fundamentals of probability, statistical, and reliability concepts, the classical methods of uncertainty quantification and analytical reliability analysis, and the state-of-the-art approaches of design optimization under uncertainty (e.g., reliability-based design optimization and robust design optimization). The topics include basic concepts of probability and distributions, uncertainty quantification using probabilistic methods, classical reliability analysis methods, time-variant reliability analysis methods, fundamentals of deterministic design optimization, reliability-based design optimization, robust design optimization, other methods of design optimization under uncertainty, and engineering applications of design optimization under uncertainty.

This book constitutes the proceedings of the 4th International Conference, IN4PL 2023, held in Rome, Italy, during November 15-17, 2023The 11 full papers and the 13 short papers included in this volume were carefully reviewed and selected from 33 submissions. The book focuses on research and development involving innovative methods, software and hardware, whereby intelligent systems are applied to industrial production and logistics. This is currently related to the concept of industry 4.0 - an expression reflecting the trend towards automation and data exchange in manufacturing technologies and processes which include cyber-physical systems, the industrial internet of things, industrial robotics, cloud computing, cognitive computing and artificial intelligence.

This book keeps an eye in the direction of applications of advanced and high performance scientific computing in describing the behavior of natural and constructed systems, e.g. chaos, bifurcation, fractal, Lyapunov exponent, period doubling, Poincaré map, strange attractor etc. With the aid of powerful computers the modem theory of chaos and its geometry, the fractals, and attractors are developed. The concepts of object oriented computing are introduced early in the text and steadily expanded as one progresses through the chapters. The beginning of each chapter is of an introductory nature, followed by practical applications, the discussion of numerical results, theoretical investigations on nonlinear stability and convergence.This is the first complete introduction to process modelling and computing that fully integrates software tools ¿ enabling professionals and students to master critical techniques hands on through computer simulations based on the popular MATLAB environment. The book offers a simple tool for all those oscillations that are travelling through the world, helping them discover its hidden beauty.Many applications as well as results of computer simulations are presented. The center of concern is set on existing as well as emerging continuous methods of investigations useful for researchers, engineers and practitioners active in many and often interdisciplinary fields, where physics, electrochemistry, biology and medicine play a key role. Coverage includes:¿ Dynamic behavior of nonlinear systems,¿ Fundamental descriptions of processes exhibiting nonlinear oscillations,¿ Mechanism and function of structures of nonlinear oscillations¿ patterns,¿ Analysis of dynamical oscillations in electric circuits and systems,¿ Artificial intelligence models of natural systems,¿ Nonlinear oscillations in chemistry, biologyand medicine,¿ Oscillations in mechanics and transport systems,¿ Oscillations in fractional-order systems,¿ Energy harvesting systems from the surrounding environment.With an insatiable appetite for exploring the surrounding world and doing research, this book can help readers quickly find ways to use new computers and facilitate the quest for greater knowledge and understanding of reality. The reach of novelty of the book ranges from new mathematical ideas to motivating questions and science issues in many subject areas.

This book offers the latest research on organizational management, knowledge management, organizational functioning, organizational digitalization, and other issues of modern company development. The book proposes to look at the pool of these problems through the prism of building various models for implementing digital transformation of business and processes. This book is intended for both young and experienced scientists who want to get acquainted with latest trends and data to use them in their research.

This book delves into optics and photonic materials, describing the development of an intelligent all-optical system capable of replicating the functional building blocks of the biological brain. Starting with an analysis of biological neuronal dynamics and traversing the state of the art of neuromorphic systems developed to date, the book arrives at a description of neural networks realized through spatial soliton technology.After a brief introduction to the biology of neural networks (Chapter 1), the book delves into the description of the neuromorphic problem emphasizing the peculiarities of optical hardware developed to date. (Chapter 2). Chapter 3 is dedicated to the description of psychomemories , which represent the modeling of human learning according to the theories of modern neuro-psychology. This chapter provides the prerequisites for understanding how solitonic neural networks (SNNs) are able to learn and how they approach biological models. Chapter 4 focuses on the experimentation of solitonic optic neurons in thin layers of lithium niobate. Optical techniques for supervised and unsupervised learning are discussed. The entire chapter is accompanied by theoretical, simulative and experimental results. This chapter explains how an X-junction neuron is able to establish synapses, modify them, or erase them. The erasure of solitonic structures represents an important innovation in the field of nonlinear optics. Finally, Chapter 5 reports on the implementation of a network of neurons capable of processing information and storing it exactly as a human episodic memory does. The chapter ends with a number of insights into the lines of research that are currently being pursued on the basis of the results obtained.The book is meant for graduate students and researchers in the fields of optics, photonic applications, and biology. However, the main beneficiaries of this book are senior researchers in the fieldof nonlinear optics and artificial intelligence. To fully understand the results, it is important to have a basic knowledge of optical physics and neuron biology.

This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13¿15, 2023.The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.

This book constitutes the refereed proceedings of the 21st International Workshop on Approximation and Online Algorithms, WAOA 2023, held in Amsterdam, The Netherlands, during September 7¿8, 2023The 16 full papers included in this book are carefully reviewed and selected from 43 submissions. The topics of WAOA 2023 were algorithmic game theory, algorithmic trading, coloring and partitioning, competitive analysis, computational advertising, computational finance, cuts and connectivity, FPT-approximation algorithms, geometric problems, graph algorithms, inapproximability results, mechanism design, network design, packing and covering, paradigms for the design and analysis of approximation and online algorithms, resource augmentation, and scheduling problems

This book constitutes the refereed proceedings of the 8th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2023, held in Varadero, Cuba, in October 2023. The 68 papers presented in the proceedings set were carefully reviewed and selected from 38 submissions. The IWAIPR conference aims to provide a leading international forum to promote and disseminate ongoing research into mathematical methods of computing techniques for Artifical Intelligence and Pattern Recognition.

This volume LNCS 14343 constitutes the refereed proceedings of the 8th ECML PKDD Workshop, AALTD 2023, in Turin, Italy, in September 2023. The 20 full papers were carefully reviewed and selected from 28 submissions. They are organized in the following topical section as follows: Machine Learning; Data Mining; Pattern Analysis; Statistics to Share their Challenges and Advances in Temporal Data Analysis.

This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied.The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary.This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.

This open access textbook introduces and defines digital humanism from a diverse range of disciplines. Following the 2019 Vienna Manifesto, the book calls for a digital humanism that describes, analyzes, and, most importantly, influences the complex interplay of technology and humankind, for a better society and life, fully respecting universal human rights.The book is organized in three parts: Part I ¿Background¿ provides the multidisciplinary background needed to understand digital humanism in its philosophical, cultural, technological, historical, social, and economic dimensions. The goal is to present the necessary knowledge upon which an effective interdisciplinary discourse on digital humanism can be founded. Part II ¿Digital Humanism ¿ a System¿s View¿ focuses on an in-depth presentation and discussion of the main digital humanism concerns arising in current digital systems. The goal of this part is to make readers aware and sensitive to these issues, including e.g. thecontrol and autonomy of AI systems, privacy and security, and the role of governance. Part III ¿Critical and Societal Issues of Digital Systems¿ delves into critical societal issues raised by advances of digital technologies. While the public debate in the past has often focused on them separately, especially when they became visible through sensational events the aim here is to shed light on the entire landscape and show their interconnected relationships. This includes issues such as AI and ethics, fairness and bias, privacy and surveillance, platform power and democracy.This textbook is intended for students, teachers, and policy makers interested in digital humanism. It is designed for stand-alone and for complementary courses in computer science, or curricula in science, engineering, humanities and social sciences. Each chapter includes questions for students and an annotated reading list to dive deeper into the associated chapter material. The book aims to provide readers with as wide an exposure as possible to digital advances and their consequences for humanity. It includes constructive ideas and approaches that seek to ensure that our collective digital future is determined through human agency.

This book constitutes the refereed proceedings of the 16th International Conference on Verification and Evaluation of Computer and Communication Systems, VECoS 2023, held in Marrakech, Morocco, during October 18¿20, 2023. The 12 full papers included in this book were carefully reviewed and selected from 36 submissions. The topics presented covered a range of subjects, including approaches to improving the scalability and efficiency of formal verification and their applications to blockchain, smart contracts and neural networks.

This book gives a detailed information of various soft computing techniques across various fields for solving relevant, real-life problems. The authors, all female leaders in the field, show how soft computing uses approximate calculations to provide imprecise yet usable solutions to complex computational problems. This enables solutions for problems that may be either unsolvable or too time-consuming to solve with current hardware. The authors show how these techniques, when applied, have proven to be efficient and robust in many difficult situations. As an important part of the Women in Science and Engineering book series, the work highlights the contribution of women leaders in soft computing, inspiring women and men, girls and boys to enter and apply themselves to secure the future in the field.

Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.

Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycleKey Features:Interpret your models' decision-making process, ensuring transparency and trust in your AI-powered solutionsGain hands-on experience in every step of the deep learning life cycleExplore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerationsPurchase of the print or Kindle book includes a free PDF eBookBook Description:Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives.This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You'll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency.As you progress, you'll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You'll also discover the transformative potential of large language models (LLMs) for a wide array of applications.By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What You Will Learn:Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs)Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your modelDeal with multi-modal data drift in a production environmentEvaluate the quality and bias of your modelsExplore techniques to protect your model from adversarial attacksGet to grips with deploying a model with DataRobot AutoMLWho this book is for:This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production.Key Features:A step-by-step tutorial towards using MXNet products to create scalable deep learning applicationsImplement tasks such as transfer learning, transformers, and more with the required speed and scalabilityAnalyze the performance of models and fine-tune them for accuracy, scalability, and speedBook Description:MXNet is an open-source deep learning framework that allows you to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in CV, NLP, and more. With this cookbook, you will be able to construct fast, scalable deep learning solutions using Apache MXNet.This book will start by showing you the different versions of MXNet and what version to choose before installing your library. You will learn to start using MXNet/Gluon libraries to solve classification and regression problems and get an idea on the inner workings of these libraries. This book will also show how to use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. You'll also learn to build and train deep-learning neural network architectures from scratch, before moving on to complex concepts like transfer learning. You'll learn to construct and deploy neural network architectures including CNN, RNN, LSTMs, Transformers, and integrate these models into your applications.By the end of the book, you will be able to utilize the MXNet and Gluon libraries to create and train deep learning networks using GPUs and learn how to deploy them efficiently in different environments.What You Will Learn:Understand MXNet and Gluon libraries and their advantagesBuild and train network models from scratch using MXNetApply transfer learning for more complex, fine-tuned network architecturesSolve modern Computer Vision and NLP problems using neural network techniquesTrain and evaluate models using GPUs and learn how to deploy themExplore state-of-the-art models with GPUs and leveraging modern optimization techniquesImprove inference run-times and deploy models in production Who this book is for:This book is ideal for Data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast, scalable deep learning solutions. The reader is expected to have a good understanding of Python programming and a working environment with Python 3.6+. A good theoretical understanding of mathematics for deep learning will be beneficial.

Navigating the Nexus: AI, Ethics, and Ethiopian CultureCONVERSATIONAL CHAT INFORMATIVE BOOKBy ABEBE- BARD AI WOLDEMARIAM Weaving together ancient wisdom and cutting-edge technology, "Navigating the Nexus" explores how AI can be harnessed to empower Ethiopia, respecting its rich cultural tapestry. By embracing values like Ubuntu and Sankofa, the book guides us through ethical considerations, from data privacy to algorithmic fairness, ensuring AI serves the common good. It showcases AI applications tackling critical challenges in healthcare, education, and agriculture, while offering solutions to bridge infrastructure and expertise gaps. Ultimately, it emphasizes the importance of public trust and community engagement, paving the way for a future where AI thrives alongside a well-informed and empowered Ethiopian society.

This book is a basic treatise on real-time computing, with particular emphasis on predictable scheduling algorithms. The main objectives of the book are to introduce the basic concepts of real-time computing, illustrate the most significant results in the field, and provide the basic methodologies for designing predictable computing systems useful in supporting critical control applications.Hard Real-Time Computing Systems is written for instructional use and is organized to enable readers without a strong knowledge of the subject matter to quickly grasp the material. Technical concepts are clearly defined at the beginning of each chapter, and algorithm descriptions are corroborated through concrete examples, illustrations, and tables. This new, fourth edition includes new sections to explain the variable-rate task model, how to improve predictability and safety in cyber-physical real-time systems that exploit machine learning algorithms, additional coverage on Response Time Analysis, and a new chapter on implementing periodic real-time tasks under Linux..