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This book starts from the classic recommendation algorithm, introduces readers to the basic principles and main concepts of this traditional algorithm, and analyzes its advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommendation systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft¿s open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommendation systems from scratch. This book is suitable not only for technical personnel in related fields such as the Internet and big data, but also for undergraduate and graduate students majoring in computer science, software engineering, and artificial intelligence.
This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.
This book constitutes the refereed proceedings of the 5th International Conference on Science of Cyber Security, SciSec 2023, held in Melbourne, VIC, Australia, during July 11¿14, 2023. The 21 full papers presented together with 6 short papers were carefully reviewed and selected from 60 submissions. The papers are organized in the topical sections named: ¿ACDroid: Detecting Collusion Applications on Smart Devices; Almost Injective and Invertible Encodings for Jacobi Quartic Curves; Decompilation Based Deep Binary-Source Function Matching.
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
This book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Évora, Portugal, during November 22¿24, 2023.The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI.The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
This book constitutes the refereed proceedings of the International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) which was held in Jeju, Korea in August, 2023. The papers of this volume are organized in topical sections on wired and wireless communication systems, high dimensional data representation and processing, networks and information security, computing techniques for efficient networks design, electronic circuits for communication systems.
This book constitutes the proceedings of the 19th International Workshop on Security and Trust Management, STM 2023, co-located with the 28th European Symposium on Research in Computer Security, ESORICS 2023, held in The Hague, The Netherlands, during September 28th, 2023 The 5 full papers together with 4 short papers included in this volume were carefully reviewed and selected from 15 submissions. The workshop presents papers with topics such as security and privacy, trust models, security services, authentication, identity management, systems security, distributed systems security, privacy-preserving protocols.
In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Klling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.
This book constitutes the refereed proceedings of 7 workshops, held at the 42nd International Conference on Conceptual Modeling, ER 2023, held in Lisbon, Portugal, during November 6-9, 2023.The 28 full and 2 short papers were carefully reviewed and selected out of 53 submissions. Topics of interest span the entire spectrum of conceptual modeling, including research and practice in areas such as theories of concepts and ontologies, techniques for transforming conceptual models into effective implementations, and methods and tools for developing and communicating conceptual models. The following workshops are included in this volume: CMLS ¿ 4th International Workshop on Conceptual Modeling for Life Sciences;CMOMM4FAIR ¿ Third Workshop on Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable, and Reusable (FAIR) Data;EmpER ¿ 6th International Workshop on Empirical Methods in Conceptual Modeling;JUSMOD ¿ Second International Workshop on Digital Justice, Digital Law and Conceptual Modeling;OntoCom ¿ 9th International Workshop on Ontologies and Conceptual Modeling;QUAMES ¿ 4th International Workshop on Quality and Measurement of Model-Driven Software Development;SmartFood ¿ First Workshop on Controlled Vocabularies and Data Platforms for Smart Food Systems.
This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9¿11, 2023.The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.
In both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as ¿dirty data.¿ Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing.Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers in the database and machine learning communities to industry practitioners.Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field.
This book constitutes the thoroughly refereed proceedings of the 11th Chinese National Conference of Social Media Processing, SMP 2023, held in Anhui, China, in November 2023.The 16 full papers presented were carefully reviewed and selected from 88 submissions. The papers are organized in the topical sections on knowledge representation and reasoning; knowledge acquisition and knowledge base construction; linked data, knowledge integration, and knowledge graph storage management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources.
This volume LNCS 14252 constitutes the refereed proceedings of 25th International Conference on Information and Communications Security, ICICS 2023, held in Tianjin, China, during November 18¿20, 2023. The 38 full papers presented together with 6 short papers were carefully reviewed and selected from 181 submissions. The conference focuses on: Symmetric-Key Cryptography; Public-Key Cryptography; Applied Cryptography; Authentication and Authorization; Privacy and Anonymity; Security and Privacy of AI; Blockchain and Cryptocurrencies; and System and Network Security.
In today's fast-paced digital landscape, organizations face an ever-increasing volume of data that holds immense potential for driving business success. However, many businesses struggle to harness this potential due to a lack of understanding and effective utilization of data within their culture. This book is a comprehensive guide that unveils the transformative power of data and provides actionable insights to cultivate a data-driven organizational culture.The book emphasizes data strategy and data governance's pivotal role in cultivating a mature data culture using practical insights, frameworks, and best practices. This approach ensures robust data culture structures that uphold data integrity, accessibility, and accountability. These structures operate on the people, processes, and technology through analytics, literacy, governance, process management, and data inventory management.The authors introduce the groundbreaking Usage and Flow Data Culture Model, a unique framework that enables organizations to evaluate and reshape their data culture based on distinct cultural types: Preservationist, Protectionist, Traditionalist, and Progressive. Each culture type is carefully dissected, revealing associated challenges and opportunities, uncovering suitable strategies in the process. Developing a worthy data culture necessitates a shift in mindset and the development of relevant skills across the organization. Building a Data Culture is your roadmap to fostering data literacy, promoting data-driven decision-making, and cultivating a data-driven mindset. What You'll LearnAssess your organization's current data cultureIdentify cultural strengths and weaknesses within your organizationDevelop a data governance programDefine data policies and standards and establish decision-making processesWho This Book is ForProfessionals and leaders across various industries who are interested in building a data culture within their organizations. The typical reader may have a background in data management, analytics, business intelligence, or technology, but the book is designed to be accessible to a wide range of readers with varying levels of expertise.
Elliot. Joe. Tommy. Nathanael. Wren. Oliver. Malik. Zach. Frank. Patrick. Noah. These are the men Margot has loved, liked, lusted over.Since she was seventeen, she¿s pictured them like stepping stones ¿ each one bringing her closer to finding someone to share her life with and, eventually, father the children she¿s always imagined in her future.From her first sexual encounter, to her first love, from grown-up dilemmas to spontaneous thrills, she¿s soaked up every experience available to her, discovering friendship, joy and despair. Through all of this she¿s refined her search until she believes she¿s arrived at `the ending¿ to her story.So how did she find herself here, single at thirty-four, and about to make the biggest decision of her life?