Machine learning Books

Book SynopsisThis two-volume set (CCIS 1762-1763) constitutes the refereed proceedings of the 4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2022, held in Bhopal, India, in December 2022. The 64 papers presented in this two-volume set were thoroughly reviewed and selected from 399 submissions. The papers are organized according to the following topical sections: ​machine learning and computational intelligence; data sciences; image processing and computer vision; network and cyber security.Table of ContentsMachine Learning and Computational Intelligence.- Data Sciences.- Image Processing and Computer Vision.- Network and Cyber Security.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.

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Book SynopsisThe multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track. Table of ContentsTime series.- Financial machine learning.- Applications.- Applications: transportation.- Demo track.

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Book SynopsisFor millennia humans have used visible marks to communicate information. Modern examples of conventional graphical symbols include written language, and non-linguistic symbol systems such as mathematical symbology or traffic signs. The latter kinds of symbols convey information without reference to language. This book presents the first systematic study of graphical symbol systems, including a history of graphical symbols from the Paleolithic onwards, a taxonomy of non-linguistic systems – systems that are not tied to spoken language – and a survey of more than 25 such systems. One important feature of many non-linguistic systems is that, as in written language, symbols may be combined into complex “messages” if the information the system represents is itself complex. To illustrate, the author presents an in-depth comparison of two systems that had very similar functions, but very different structure: European heraldry and Japanese kamon. Writing first appeared in Mesopotamia about 5,000 years ago and is believed to have evolved from a previous non-linguistic accounting system. The exact mechanism is unknown, but crucial was the discovery that symbols can represent the sounds of words, not just the meanings. The book presents a novel neurologically-inspired hypothesis that writing evolved in an institutional context in which symbols were “dictated”, thus driving an association between symbol and sound, and provides a computational simulation to support this hypothesis. The author further discusses some common fallacies about writing and non-linguistic systems, and how these relate to widely cited claims about statistical “evidence” for one or another system being writing. The book ends with some thoughts about the future of graphical symbol systems. The intended audience includes students, researchers, lecturers, professionals and scientists from fields like Natural Language Processing, Machine Learning, Archaeology and Semiotics, as well as general readers interested in language and/or writing systems and symbol systems.Trade Review“The book is the first systematic study of graphical symbol systems, ranging from the imagery found in Paleolithic cave paintings, through ancient and contemporary writing systems employing both phonetic and logographic symbols, to modern language-independent symbols such as meteorological icons and emoji.” (Andrew Robinson, Science, science.org, Vol. 382 (6669), October 27, 2023)Table of ContentsPreface1 Introduction 1.1 What’s in a Symbol? 1.2 Syntax 1.3 What this book is about 2 Semiotics 2.1 Introduction 2.2 The Field of Semiotics 2.3 Iconicity 2.4 Syntax 2.5 Articulation 3 Taxonomy 3.1 Introduction 3.2 History 3.3 Preliminary Taxonomy 3.4 Examples of systems 3.5 Kamon/Heraldry 3.5.1 Kamon 3.5.2 British heraldry 3.5.3 Structural Differences: Summary 3.A Symbol system survey (A detailed analysis of 26 symbol systems) 3.B Statistics of kamon 4 Writing Systems 4.1 Introduction 4.2 Writing 4.2.1 Preliminaries 4.2.2 Types of Writing Systems 4.2.3 Blissymbolics 4.3 Limitations of writing 4.3.1 Inclusiveness 4.3.2 Graphocentrism 4.3.3 Summary 4.4 Writing: A summary 5 Symbols in the Brain 5.1 Brain areas 5.2 Meaning in the brain 5.3 Reading in the brain 5.3.1 The letterbox 5.3.2 Summary: the evolution of the letterbox 5.4 Non-linguistic symbols in the brain 5.5 A Hypothesis 6 The Evolution of Writing 6.1 Evolution 6.2 A Hypothesis 6.3 Schools 7 Simulations 7.1 Prior work 7.2 Simulation 7.2.1 Description of the model 7.2.2 Simulation of evolution 7.2.3 Summary and discussion 7.3 Pre-writing 7.4 Summary 7.A Details 7.A.1 Data Generation 7.A.2 Model 7.B Compounds 7.B.1 Monosyllabic cases 7.B.2 Sesquisyllabic cases 7.B.3 Disyllabic cases 8 Misrepresentations 8.1 Introduction 8.2 What does it mean to say something "Looks like writing"? 8.3 Statistics 8.3.1 Statistical analysis of the Indus Valley inscriptions 8.3.2 More on structure in the Indus inscriptions 8.3.3 Variations of distributions of symbols 8.4 Summary 9 The Future 9.1 The Dream of a Universal Written Language 9.2 Semasiography 9.3 The Prestige of Writing 9.4 Final Thoughts

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Book SynopsisThe three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023. The 65 full papers, 41 short papers, 19 demonstration papers, 12 reproducibility papers consortium papers, 7 tutorial papers, and 10 doctorial consortium papers were carefully reviewed and selected from 489 submissions. The book also contains, 8 workshop summaries and 13 CLEF Lab descriptions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.Table of ContentsFull Papers.- Automatic Summarization of Financial Earnings Calls Transcript.- Parameter-Efficient Sparse Retrievers and Rerankers using Adapters.- Feature Differentiation and Fusion for Semantic Text Matching.- Multivariate Powered Dirichlet-Hawkes Process.- Fragmented Visual Attention in Web Browsing: Weibull Analysis of Item Visit Times.- Topic-Enhanced Personalized Retrieval-based Chatbot.- Improving the Generalizability of the Dense Passage Retriever Using Generated Datasets.- SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval.- Knowing What and How: A Multi-modal Aspect-Based Framework for Complaint Detection.- What is your cause for concern? Towards Interpretable Complaint Cause Analysis.- DeCoDE: DEtection of COgnitive Distortion and Emotion cause extraction in clinical conversations.- Domain-aligned Data Augmentation for Low-resource and Imbalanced Text Classification.- Privacy-Preserving Fair Item Ranking.- Multimodal Geolocation Estimation of News Photos.- Topics in Contextualised Attention Embeddings.- New Metrics to Encourage Innovation and Diversity in Information Retrieval Approaches.- Probing BERT for Ranking Abilities.- Clustering of Bandit with Frequency-Dependent Information Sharing.- Contrastive Graph Learning with Positional Representation for Recommendation.- Domain Adaptation for Anomaly Detection on Heterogeneous Graphs in E-Commerce.- Short PapersImproving Neural Topic Models with Wasserstein Knowledge Distillation.- Towards Effective Paraphrasing for Information Disguise.- Generating Topic Pages for Scientific Concepts Using Scientific Publications.- Relevance Judgements for Fair Ranking.- A Study of Term-Topic Embeddings for Ranking.- Topic Refinement in Multi-Level Hate Speech Detection.- Is Cross-modal Information Retrieval Possible without Training?.- Adversarial Adaptation for French Named Entity Recognition.- Exploring Fake News Detection with Heterogeneous Social Media Context Graphs.- Justifying Multi-Label Text Classifications for Healthcare Applications.- Doc2Query–: When Less is More.- Towards Quantifying The Privacy Of Redacted Text. -Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study.- Leveraging Comment Retrieval for Code Summarization.- CPR: Cross-domain Preference Ranking with User Transformation.- Colbert-FairPRF: Towards Fair Pseudo-Relevance Feedback in Dense Retrieval.- C2LIR: Continual Cross-lingual Transfer for Low-Resource Information Retrieval.- Joint Extraction and Classification of Danish Competences for Job Matching.- A Study on FGSM Adversarial Training for Neural Retrieval.- Dialogue-to-Video Retrieval.- Time-dependent next-basket recommendations.- Investigating the Impact of Query Representation on Medical Information Retrieval.- Where a Little Change Makes a Big Difference: A Preliminary Exploration of Children’s Queries.- Multi-document QA with GPT-3 and Neural Reranking .- Towards Detecting Interesting Ideas Expressed in Text.- Towards Linguistically Informed Multi-Objective Transformer Pre-Training for Natural Language Inference.- Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks.- Consumer Health Question Answering Using Off-the-shelf Components.- MOO-CMDS+NER: Named Entity Recognition-based Extractive Comment-oriented Multi-document Summarization.- Don’t Raise Your Voice, Improve Your Argument: Learning to Retrieve Convincing Arguments.- Learning Query-Space Document Representations for High-Recall Retrieval.- Investigating Conversational Search Behavior For Domain Exploration.- Evaluating Humorous Response Generation to Playful Shopping Requests.- Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents.- Trigger or not Trigger: Dynamic Thresholding for Few Shot Event Detection.- The Impact of a Popularity Punishing Hyperparameter on ItemKNN Recommendation Performance.- Neural Ad hoc Retrieval Meets Information Extraction.- Augmenting Graph Convolutional Networks with Textual Data for Recommendations.- Utilising Twitter Metadata for Hate Classification.- Evolution of Filter Bubbles and Polarization in News Recommendation.- Capturing Cross-platform Interaction for Identifying Coordinated Accounts of Misinformation Campaigns.

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Book SynopsisThis book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2.Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.Table of ContentsAn Introduction to Neural Networks.- The Backpropagation Algorithm.- Machine Learning with Shallow Neural Networks.- Deep Learning: Principles and Training Algorithms.- Teaching a Deep Neural Network to Generalize.- Radial Basis Function Networks.- Restricted Boltzmann Machines.- Recurrent Neural Networks.- Convolutional Neural Networks.- Graph Neural Networks.- Deep Reinforcement Learning.- Advanced Topics in Deep Learning.

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Book SynopsisThis book is a comprehensive guide to the latest developments in data-driven additive manufacturing (AM). From data mining and pre-processing to signal processing, computer vision, and more, the book covers all the essential techniques for preparing AM data. Readers willl explore the key physical and synthetic sources of AM data throughout the life cycle of the process and learn about feature engineering techniques, pipelines, and resulting features, as well as their applications at each life cycle phase. With a focus on featurization efforts from reviewed literature, this book offers tabular summaries for major data sources and analyzes feature spaces at the design, process, and structure phases of AM to uncover trends and insights specific to feature engineering techniques. Finally, the book discusses current challenges and future directions, including AI/ML/DL readiness of AM data. Whether you're an expert or newcomer to the field, this book provides a broader summary of the status and future of data-driven AM technology.Table of ContentsIntroduction.- Feature Engineering in AM.- Applications in Data-driven AM.- Analyzing AM Feature Spaces.- Challenges and Opportunities in AM Data Preparation.- Summary.

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Book SynopsisThis book explores and demonstrates how geometric tools can be used in data analysis. Beginning with a systematic exposition of the mathematical prerequisites, covering topics ranging from category theory to algebraic topology, Riemannian geometry, operator theory and network analysis, it goes on to describe and analyze some of the most important machine learning techniques for dimension reduction, including the different types of manifold learning and kernel methods. It also develops a new notion of curvature of generalized metric spaces, based on the notion of hyperconvexity, which can be used for the topological representation of geometric information.In recent years there has been a fascinating development: concepts and methods originally created in the context of research in pure mathematics, and in particular in geometry, have become powerful tools in machine learning for the analysis of data. The underlying reason for this is that data are typically equipped with some kind of notion of distance, quantifying the differences between data points. Of course, to be successfully applied, the geometric tools usually need to be redefined, generalized, or extended appropriately.Primarily aimed at mathematicians seeking an overview of the geometric concepts and methods that are useful for data analysis, the book will also be of interest to researchers in machine learning and data analysis who want to see a systematic mathematical foundation of the methods that they use. Table of ContentsIntroduction.- Topological foundations, hypercomplexes and homology.- Weighted complexes, cohomology and Laplace operators.- The Laplace operator and the geometry of graphs.- Metric spaces and manifolds.- Linear methods: Kernels, variations, and averaging.- Nonlinear schemes: Clustering, feature extraction and dimension reduction.- Manifold learning, the scheme of Laplacian eigenmaps.- Metrics and curvature.

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Book SynopsisThis book constitutes the proceedings of the 17th International Conference on Formal Concept Analysis, ICFCA 2023, which took place in Kassel, Germany, in July 2023.The 13 full papers presented in this volume were carefully reviewed and selected from 19 submissions. The International Conference on Formal Concept Analysis serves as a platform for researchers from FCA and related disciplines to showcase and exchange their research findings. The papers are organized in two topical sections, first "Theory" and second "Applications and Visualization".Table of Contents​Theory: Approximating fuzzy relation equations through concept lattices.- Doubly-Lexical Order Supports Standardisation and Recursive Partitioning of Formal Context.- Graph-FCA Meets Pattern Structures.- On the commutative diagrams among Galois connections involved in closure structures.- Scaling Dimension.- Three Views on Dependency Covers from an FCA Perspective.- A Triadic Generalisation of the Boolean Concept Lattice.- Applications and Visualization: Computing witnesses for centralising monoids on a three-element set.- Description Quivers for Compact Representation of Concept Lattices and Ensembles of Decision Trees.- Examples of clique closure systems.- On the maximal independence polynomial of the covering graph of the hypercube up to n=6.- Relational Concept Analysis in Practice: Capitalizing on Data Modeling using Design Patterns.- Representing Concept Lattices with Euler Diagrams.

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Book SynopsisThis two-volume set LNCS 14193-14194 constitutes the proceedings of International Workshops co-located with the 17th International Conference on Document Analysis and Recognition, ICDAR 2023, held in San José, CA, USA, during August 21–26, 2023. The total of 43 regular papers presented in this book were carefully selected from 60 submissions. Part I contains 22 regular papers that stem from the following workshops: ICDAR 2023 Workshop on Computational Paleography (IWCP); ICDAR 2023 Workshop on Camera-Based Document Analysis and Recognition (CBDAR); ICDAR 2023 International Workshop on Graphics Recognition (GREC); ICDAR 2023 Workshop on Automatically Domain-Adapted and Personalized Document Analysis (ADAPDA); Part II contains 21 regular papers that stem from the following workshops: ICDAR 2023 Workshop on Machine Vision and NLP for Document Analysis (VINALDO); ICDAR 2023 International Workshop on Machine Learning (WML). Table of ContentsTypefaces and Ligatures in Printed Arabic Text: A Deep Learning-Based OCR Perspective.- Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction.- Extracting Key-Value Pairs in Business Documents.- Long-Range Transformer Architectures for Document Understanding.-Pre-training transformers for Corporate Documents Understanding.- Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive Text.- Arxiv Tables: Document Understanding Challenge Linking Texts and Tables.- Subgraph-Induced Extraction Technique for Information (SETI) from Administrative Documents.- Document Layout Annotation: Database and Benchmark in the Domain of Public Affairs.- A Clustering Approach Combining Lines and Text Detection for Table Extraction.- Absformer: Transformer-Based Model for Unsupervised Multi-Document Abstractive Summarization.- A Comparison of Demographic Attributes Detection from Handwriting Based on Traditional and Deep Learning Methods.- A New Optimization Approach to Improve an Ensemble Learning Model: Application to Persian/Arabic Handwritten Character Recognition.- BN-DRISHTI: Bangla Document Recognition Through Instance-level Segmentation of Handwritten Text Images.- Text Line Detection and Recognition of Greek Polytonic Documents.- A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet.- Local Style Awareness of Font Images.- Fourier Feature-Based CBAM and Vision Transformer for Text Detection in Drone Images.- Document Binarization with Quaternionic Double Discriminator Generative Adversarial Network.- Crosslingual Handwritten Text Generation Using GANs.- Knowledge Integration inside Multitask Network for Analysis of Unseen ID Types.

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Book SynopsisThis book constitutes the refereed proceedings of the second International Workshop on Cancer Prevention through Early Detection, CaPTion, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023.The 11 papers presented at CaPTion 2023 were carefully reviewed and selected from 12 submissions. The workshop invites researchers to submit their work in the field of medical image analysis around the central theme of cancer and early cancer detection, progression, inflammation understanding, multimodality data, and computer-aided navigation.

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Book SynopsisThis book constitutes the refereed proceedings of the ​Second Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2023 held in conjunction with MICCAI 2023, held in Vancouver, BC, Canada, on October 2, 2023. The 10 full papers presented in this book were carefully peer reviewed and selected from 15 submissions. The second workshop on AIIIMA, similarily to the first, aimes to create a forum to discuss the specific sub-topic of AI over Infrared Images for Medical Applications at MICCAI and promote this novel area of research, that has the potential to hugely impact our society, among the research community.Table of ContentsArtificial Intelligence over Infrared Images for Medical Applications.- The Socioeconomic Impact of Artificial Intelligence Applications in Diagnostic Medical Thermography: A Comparative Analysis with Mammography in Breast Cancer Detection and other diseases early detection.- 3D-BreastNet: A Self-supervised Deep Learning Network for Reconstruction of 3D Breast Surface from 2D Thermal Images.- Modeling the 3D Breast Surface Using Thermography.- 3D Convolutional Neural Networks for Dynamic Breast Infrared Imaging Classification.- Could the consideration of symmetry be statistically significant for breast infrared analysis?.- Performance Evaluation of Convolutional Segmentation Models with Human Hand Thermal Images (H2TI) Dataset.- A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces.- Relationship between thermography assessment and hamstring isometric test in amateur soccer players.- Evaluation of Deep Learning Models for Lower Extremity Muscle Segmentation in Thermal Imaging.- Radiomics feature selection from Thyroid thermal images to improve thyroid nodules interpretations.

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Book SynopsisThis volume comprises the proceedings of the International Workshop, ShapeMI 2023, which took place alongside MICCAI 2023 on October 8, 2023, in Vancouver, British Columbia, Canada.The 23 selected full papers deal with all aspects of leading methods and applications for advanced shape analysis and geometric learning in medical imaging.Table of ContentsAnatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction.- C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy.- Anatomy-Aware Masking for Inpainting in Medical Imaging.- Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.- Optimal coronary artery segmentation based on transfer learning and UNet architecture.- Unsupervised Learning of Cortical Surface Registration using Spherical Harmonics.- Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications.- ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.- Body Fat Estimation from Surface Meshes using Graph Neural Networks.- Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors.- On the Localization of Ultrasound Image Slices within Point Distribution Models.- FSJP-Net: Foreground and Shape Joint Perception Network for Glomerulus Detection.- Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models.- Muscle volume quantification: guiding transformers with anatomical priors.- Geodesic Logistic Analysis of Lumbar Spine Intervertebral Disc Shapes in Supine and Standing Positions.- SlicerSALT: From medical images to quantitative insights of anatomy.- Predicting Shape Development: A Riemannian Method.- AReg IOS: Automatic Registration on IntraOralScans.- Modeling Longitudinal Optical Coherence Tomography Images for Monitoring and Analysis of Glaucoma Progression.- IcoConv : Explainable brain cortical surface analysis for ASD classification.- DeCA: A Dense Correspondence Analysis Toolkit for Shape Analysis.- 3D Shape Analysis of Scoliosis.- SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction.

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Book SynopsisThis book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments. The authors present the latest developments and techniques for building such continual learning dialogue systems. The book explains how these developments allow systems to continuously learn new language expressions, lexical and factual knowledge, and conversational skills through interactions and dialogues. Additionally, the book covers techniques to acquire new training examples for learning new tasks during the conversation. The book also reviews existing work on lifelong learning and discusses areas for future research. Table of Contents1 Introduction.- 2 Open-world Continual Learning: A Framework.- 3 Continuous Factual Knowledge Learning in Dialogues.- 4 Continuous and Interactive Language Learning and Grounding.- 5 Continual Learning in Chit-chat Systems.- 6 Continual Learning for Task-oriented Dialogue Systems.- 7 Continual Learning of Conversational Skills.- 8 Conclusion and Future Directions.

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Book SynopsisThis book constitutes the proceedings of the 11th International Conference on Big Data and Artificial Intelligence, BDA 2023, held in Delhi, India, during December 7–9, 2023. The17 full papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: ​Keynote Lectures, Artificial Intelligence in Healthcare, Large Language Models, Data Analytics for Low Resource Domains, Artificial Intelligence for Innovative Applications and Potpourri. Table of Contents​Keynote Lectures.- Representation Learning for Dialog Models.- Sparsity, modularity, and structural plasticity in deep neural networks.- Artificial Intelligence in Healthcare.- Tuberculosis disease diagnosis using controlled super resolution.- GREAT AI in Medical Appropriateness and Value-Based-Care.- Large Language Models.- KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models.- SciPhyRAG - Retrieval Augmentation to Improve LLMs on Physics Q&A.- Revolutionizing High School Physics Education: A Novel Dataset.- Context-Enhanced Language Models for Generating Multi-Paper Citations.- GEC-DCL: Grammatical Error Correction Model with Dynamic Context Learning for Paragraphs & Scholarly Papers.- Data Analytics for Low Resource Domains.- A Deep Learning Emotion Classification Framework for Low Resource Languages.- Assessing the Efficacy of Synthetic Data for Enhancing Machine Translation Models in Low Resource Domains.- Artificial Intelligence for Innovative Applications.- Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks.- IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI.- Ensemble-Based Road Surface Crack Detection: A Comprehensive Approach.- Potpourri.- Fast similarity search in large-scale Iris databases using high-dimensional hashing.- Explaining Finetuned Transformers on Hate Speech Predictions using Layerwise Relevance Propagation.- Multilingual Speech Sentiment Recognition using Spiking Neural Networks.- FopLAHD: Federated optimization using Locally Approximated Hessian Diagonal.- A Review of Approaches on Facets for Building IT-based Career Guidance Systems.

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Book SynopsisThe book is structured into thirteen chapters; each comes with its own dedicated contributions and future research directions. Chapter 1 introduces IoT and the use of Edge computing, particularly cloud computing, and mobile edge computing. This chapter also mentions the use of edge computing in various real-time applications such as healthcare, manufacturing, agriculture, and transportation. Chapter 2 motivates mathematical modeling for federated learning systems with respect to IoT and its applications. Further Chapter 3 extends the discussion of federated learning for IoT, which has emerged as a privacy-preserving distributed machine learning approach. Chapter 4 provides various machine learning techniques in Industrial IoT to deliver rapid and accurate data analysis, essential for enhancing production quality, sustainability, and safety. Chapter discusses the potential role of data-driven technologies, such as Artificial Intelligence, Machine Learning, and Deep Learning, focuses on their integration with IoT communication technologies. Chapter 6 presents the requirements and challenges to realize IoT deployments in smart cities, including sensing infrastructure, Artificial Intelligence, computing platforms, and enabling communications technologies such as 5G networks. To highlight these challenges in practice, the chapter also presents a real-world case study of a city-scale deployment of IoT air quality monitoring within Helsinki city. Chapter 7 uses digital twins within smart cities to enhance economic progress and facilitate prompt decision-making regarding situational awareness. Chapter 8 provides insights into using Multi-Objective reinforcement learning in future IoT networks, especially for an efficient decision-making system. Chapter 9 offers a comprehensive review of intelligent inference approaches, with a specific emphasis on reducing inference time and minimizing transmitted bandwidth between IoT devices and the cloud. Chapter 10 summarizes the applications of deep learning models in various IoT fields. This chapter also presents an in-depth study of these techniques to examine new horizons of applications of deep learning models in different areas of IoT. Chapter 11 explores the integration of Quantum Key Distribution (QKD) into IoT systems. It delves into the potential benefits, challenges, and practical considerations of incorporating QKD into IoT networks. In chapter 12, a comprehensive overview regarding the current state of quantum IoT in the context of smart healthcare is presented, along with its applications, benefits, challenges, and prospects for the future. Chapter 13 proposes a blockchain-based architecture for securing and managing IoT data in intelligent transport systems, offering advantages like immutability, decentralization, and enhanced security.Table of ContentsChapter. 1. Edge Computing for IoTChapter. 2. Federated Learning Systems: Mathematical modelling and Internet of ThingsChapter. 3. Federated Learning for Internet of ThingsChapter. 4. Machine Learning Techniques for Industrial Internet of ThingsChapter. 5. Exploring IoT Communication Technologies and Data-Driven SolutionsChapter. 6. Towards Large-Scale IoT Deployments in Smart Cities: Requirements and ChallengesChapter. 7. Digital Twin and IoT for Smart City MonitoringChapter. 8. Multiobjective and Constrained Reinforcement Learning for IoTChapter. 9. Intelligence Inference on IoT DevicesChapter. 10. Applications of Deep Learning models in diverse streams of IoTChapter. 11. Quantum Key Distribution in Internet of ThingsChapter. 12. Quantum Internet of Things for Smart HealthcareChapter. 13. Enhancing Security in Intelligent Transport Systems: A Blockchain-Based Approach for IoT Data ManagementIndex

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Book SynopsisChapter. 1. Probability and Statistics: An Introduction.- Chapter. 2. Summarizing and Visualizing Data.- Chapter. 3. Probability Basics and Random Variables.- Chapter. 4. Probability Distributions.- Chapter. 5. Hypothesis Testing and Confidence Intervals.- Chapter. 6. Reconstructing Probability Distributions from Data.- Chapter. 7. Regression.- Chapter. 8. Classification: A Probabilistic View.- Chapter. 9. Unsupervised Learning: A Probabilistic View.- Chapter. 10. Discrete State Markov Processes.- Chapter. 11. Probabilistic Inequalities and Extreme Value Analysis.- Bibliography.- Index.

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Book SynopsisThe two-volume set LNCS 15271 and 15272 constitutes the proceedings of the 4th Energy Informatics Academy Conference, EI.A 2024, held inKuta, Bali, Indonesia, during October 2325, 2024. The 40 full papers and 8 short papers included in these proceedings were carefully reviewed and selected from 64 submissions. They are categorized under the topical sections as follows:Part I:IoT Edge Computing, and Software Innovations in Energy,Big Data Analytics and Cybersecurity in Energy,Digital Twin Technology and Energy Simulations,Energy data and consumer behaviors, and Digitalization of District Heating and Cooling Systems. Part II:Smart Buildings and Energy Communities,Energy Pricing, Trading, and Market Dynamics,Demand Flexibility and Energy Conservation Strategies,Optimization of Energy Systems and Renewable Integration andEnergy System Resilience and Reliability. Chapter 14 and chapter 15 is available open access under a Creative Commons Attribution 4.0 International License vialink.springer.com.

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Book SynopsisChapter 1 Introduction.- Chapter 2 Learning and Decision Making Process.- Chapter 3 Fuzzy Logic Inference System.- Chapter 4 Introduction to Machine Learning.- Chapter 5 Introduction to Regression Algorithms.- Chapter 6 Introduction to Classification Algorithms.- Chapter 7 Neural Networks and Deep Learning.- Chapter 8 Introduction to Unsupervised Learning.- Chapter 9 Introduction to Reinforcement Learning.- Chapter 10 Introduction to Adaptive Neuro Fuzzy Inference System.- Chapter 11 Case Study Projects on Fuzzy Logic Technology.- Chapter 12 Case Study Projects on Deep Learning.- Appendix A.

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Book SynopsisIntroduction.- Domain-informed Feature Engineering for Smart Manufacturing.- Domain-informed.- Dimension Reduction for Smart Manufacturing.- Fabrication-Aware Machine.- Learning Models for Additive Manufacturing.- Domain-Informed Machine Learning.- Models for Nanomanufacturing.- Engineering-Informed Transfer Learning.- Engineering-Informed.- Process Compensation and Adjustment.- Domain-informed Data Pre-Processing in Additive Manufacturing.- Future Perspective for Domain-informed Machine.- Learning for Smart Manufacturing.

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Book SynopsisPreface.- 1 Linear Algebra and Optimization: An Introduction.- 2 Linear Transformations and Linear Systems.- 3 Eigenvectors and Diagonalizable Matrices.- 4 Optimization Basics: A Machine Learning View.- 5 Advanced Optimization Solutions.- 6 Constrained Optimization and Duality.- 7 Singular Value Decomposition.- 8 Matrix Factorization.- 9 The Linear Algebra of Similarity.- 10 The Linear Algebra of Graphs.- 11 Optimization in Computational Graphs.- Index.

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Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.

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Book SynopsisThe book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.

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Book SynopsisThe definitive guide to the game-theoretic and probabilistic underpinning for Bitcoin’s security model. The book begins with an overview of probability and game theory. Nakamoto Consensus is discussed in both practical and theoretical terms. This volume: Describes attacks and exploits with mathematical justifications, including selfish mining. Identifies common assumptions such as the Market Fragility Hypothesis, establishing a framework for analyzing incentives to attack. Outlines the block reward schedule and economics of ASIC mining. Discusses how adoption by institutions would fundamentally change the security model. Analyzes incentives for double-spend and sabotage attacks via stock-flow models. Overviews coalitional game theory with applications to majority takeover attacks Presents Nash bargaining with application to unregulated environments This book is intended for students or researchers wanting to engage in a serious conversation about the future viability of Bitcoin as a decentralized, censorship-resistant, peer-to-peer electronic cash system. 　

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Book SynopsisThis book explores the significance, challenges and benefits of digital twin technologies; it focuses in particular on various architectures, applications and challenges in the implementation of digital twins to Machine Learning and Internet of Things capabilities. Through the analysis of smart city and smart manufacturing case studies, the book explores the benefits of digital technologies in the Industry 4.0 Era.

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Book SynopsisThis book is to explore and explain the strategically sound capabilities at the synchronization between quantum computing and artificial intelligence (AI). The reader will be presented with an introduction and a deeper review of the technological trends and transitions being unearthed in the quantum computing and AI domains.

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Book SynopsisQuantum computers have demonstrated that they have the inherent potential to outperform classical computers in many areas. One of the major impacts is that the currently available cryptography algorithms are bound to no longer hold once quantum computers are able to compute at full speed. This book presents an overview of all the cross-disciplinary developments in cybersecurity that are being generated by the advancements in quantum computing.

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Book SynopsisArtifi cial Intelligence (AI) has been an exciting fi eld of study and research in educational institutions and research labs across the globe. Technology giants and IT organizations invest heavily on AI technologies and tools with the aim of preciselyautomating a variety of simple as well as complicated business operations acrossindustry verticals. This book covers the latest trends and transitions happening in thefuturistic AI domain. The book also focuses on machine and deep learning (ML/DL)algorithms, which are, undoubtedly, the mainstream implementation technologies ofstate-of-the-art AI systems and services. Also, there are chapters on computer vision(CV) and natural language processing (NLP), the primary use cases and applicationsof AI. The book has well-written chapters for demystifying AI model engineeringmethods. Further on, our esteemed readers can fi nd details on AI model evaluation,optimization, deployment and observability. Finally, the book deals and describesgenerative AI, the latest buzzword in the IT industry. The book presents the recent ground-breaking changes taking place in the aspects of AI model building, hosting, running and maintaining in cloud environments, articulates and accentuates the most recent developments taking place in the domain of Artifi cial Intelligence, covers the noteworthy innovations and disruptions towards Generative Artifi cial Intelligence (Generative AI), explains the breakthrough innovations and disruptions towards Artifi cial General Intelligence (AGI) and delineates an engaging discussion of Natural Language Processing, Neuromorphic Systems and Biometrics.

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Book SynopsisThis book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.Table of ContentsIntroduction.- Basics of Sensory Data.- Feature Engineering based on Sensory Data.- Predictive Modeling without Notion of Time.- Predictive Modeling with Notion of Time.- Reinforcement Learning to Provide Feedback and Support.- Discussion.

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Book SynopsisText analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.Trade Review“The book discusses many key technologies used today in social media, such as opinion mining or event detection. One of the most promising new technologies, deep learning, is discussed as well. This book is an excellent resource for programmers and graduate students interested in becoming experts in the text mining field. … Summing Up: Recommended. Graduate students, researchers, and professionals.” (J. Brzezinski, Choice, Vol. 56 (04), December, 2018)Table of Contents1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Text Sequence Modeling and Deep Learning.- 11 Text Summarization.- 12 Information Extraction.- 13 Opinion Mining and Sentiment Analysis.- 14 Text Segmentation and Event Detection.

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With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

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Book SynopsisAlgorithmen für künstliches Lernen verstehen Maschinelles Lernen ist eines der wichtigsten Teilgebiete der künstlichen Intelligenz und das Verstehen und Entwickeln von passenden Algorithmen bleibt die große Herausforderung. Dieses Buch bietet einen außergewöhnlich umfassenden Überblick über die neuesten Algorithmen und die bereits bewährten Verfahren. Jörn Fischer beschreibt nicht nur deren Funktionsweise, sondern gibt für alle Bereiche verständliche Beispiele, die detailliert beschrieben und leicht nachvollziehbar sind. Außerdem werden hilfreiche Methoden zur Fehlersuche und -beseitigung an die Hand gegeben. Sie erfahren Wie Sie mit Python und Frameworks startenWie Sie Optimierung, Clustering und Klassifizierung umsetzen Wie generative Methoden und Reinforcement Learning funktionieren Wie neuronale Netze arbeiten und erklärbar werden

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Book SynopsisMarkus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons. Table of ContentsClassification and Regression Approaches.- Applications and Affective Corpora.- Modalities and Feature Extraction.- Machine Learning for the Estimation of Affective Dimensions.- Adaptation and Personalization of Classifiers.- Experimental Validation.

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Book SynopsisThis book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Reinforcement Learning has proven its capabilities in different challenging optimization problems and is now an established method in Operations Research. However, complex attacker-defender scenarios have several characteristics that challenge Reinforcement Learning algorithms, requiring enormous computational power to obtain the optimal solution. The upcoming field of Quantum Computing is a promising path for solving computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning. It proposes a novel quantum REINFORCE algorithm that enhances its classical counterpart by Quantum Variational Circuits. The new algorithm is compared to classical algorithms regarding the convergence speed and memory usage on several attacker-defender scenarios with increasing complexity. In addition, to study its applicability on today's NISQ hardware, the algorithm is evaluated on IBM's quantum computers, which is accompanied by an in-depth analysis of the advantages of Quantum Reinforcement Learning.Table of ContentsMotivation: Complex Attacker-Defender Scenarios - The eternal conflict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman’s Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing.- Approximation in Quantum Computing.- Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning.- Applying Quantum REINFORCE to the Information Game.- Evaluating quantum REINFORCE on IBM’s Quantum Hardware.- Future Steps in Quantum Reinforcement Learning for Complex Scenarios.- Conclusion.

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Book SynopsisFatigue failure of structures used in transportation, industry, medical equipment, and electronic components needs to build a link between cutting-edge experimental characterization and probabilistically grounded numerical and artificially intelligent tools. The physics involved in this process chain is computationally prohibitive to comprehend using traditional computation methods. Using machine learning and Bayesian statistics, a defect-correlated estimate of fatigue strength was developed. Fatigue, which is a random variable, is studied in a Bayesian-based machine learning algorithm. The stress-life model was used based on the compatibility condition of life and load distributions. The defect-correlated assessment of fatigue strength was established using the proposed machine learning and Bayesian statistics algorithms. It enabled the mapping of structural and process-induced fatigue characteristics into a geometry-independent load density chart across a wide range of fatigue regimes.Table of ContentsIntroduction and objectives.- Background on process-property relationship.- Training and testing data.- Estimation of lifetime trends based on FEM.- Bayesian inferences of fatigue-related influences.- Summary and outlook.- References.

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Book SynopsisMachine learning has become key in supporting decision-making processes across a wide array of applications, ranging from autonomous vehicles to malware detection. However, while highly accurate, these algorithms have been shown to exhibit vulnerabilities, in which they could be deceived to return preferred predictions. Therefore, carefully crafted adversarial objects may impact the trust of machine learning systems compromising the reliability of their predictions, irrespective of the field in which they are deployed. The goal of this book is to improve the understanding of adversarial attacks, particularly in the malware context, and leverage the knowledge to explore defenses against adaptive adversaries. Furthermore, to study systemic weaknesses that can improve the resilience of machine learning models. Table of ContentsThe Beginnings of Adversarial ML.- Framework for Adversarial Malware Evaluation.- Problem-Space Attacks.- Feature-Space Attacks.- Closing Remarks.

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Book SynopsisThe LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 45th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers. Topics covered include data analysis, information extraction, blockchains, and big data.Table of ContentsInteroperable Data Extraction and Analytics Queries over Blockchains.- Exploiting Twitter for Informativeness Classification in Disaster Situations.- COTILES: Leveraging Content and Structure for Evolutionary Community Detection.- A Weighted Feature-Based Image Quality Assessment Framework in Real-Time.- Sharing Knowledge in Digital Ecosystems Using Semantic Multimedia Big Data.- Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments Using Web and Microservice Technologies.- Stable Marriage Matching for Homogenizing Load Distribution in a Cloud Data Center.- A Sentiment Analysis Software Framework for the Support of Business Information Architecture in the Tourist Sector

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