Andreas Dengel – författare

This volume constitutes the refereed proceedings of the 9th International Conference of the Immersive Learning Network, iLRN 2023, held in San Luis Obispo, USA, in June 2023 as a hybrid event.The 26 revised full papers and 13 shprt papers presented in this volume were carefully reviewed and selected from 110 submissions.

This book constitutes the proceedings of the10th International Conference on Immersive Learning, iLRN 2024, held in Glasgow, UK during June 10–13, 2024. The 30 full papers and 13 short papers presented in this volume were carefully reviewed and selected from 144 submissions.

This book constitutes the proceedings of the10th International Conference on Immersive Learning, iLRN 2024, held in Glasgow, UK during June 10–13, 2024. The 30 full papers and 13 short papers presented in this volume were carefully reviewed and selected from 144 submissions.

Molecular biology is at the forefront of scientific discovery, unraveling the intricacies of life at the most fundamental level. As biological systems become increasingly complex and data-rich, artificial intelligence (AI) has emerged as a pivotal tool for unlocking new insights and enhancing our understanding of these systems. This first volume focuses on the core principles of molecular biology while introducing AI-driven approaches to genomic and proteomic sequence analysis. It serves as a foundation for integrating computational methodologies into the study of biological systems.The chapters in this volume are structured to provide a comprehensive overview of the essential concepts, tools, and methodologies in molecular biology, enriched by the latest advancements in AI:Fundamentals of Molecular Biology: This chapter delves into the foundational elements of molecular biology, exploring the central dogma, gene expression regulation, cellular organization, and the evolution of genome studies. It also highlights the role of computational biology in complementing traditional experimental techniques.DNA, RNA, & Protein Structures: Understanding the structural intricacies of DNA, RNA, and proteins is crucial for comprehending their functions. This chapter outlines their fundamental properties and sets the stage for discussing AI-driven sequence analysis.Exploration of AI-Driven Genomic and Proteomic Sequence Analysis Landscape: This section provides an in-depth look at how AI is reshaping the field of sequence analysis. Topics include representation learning, feature engineering, predictive modeling, and an evaluation of performance metrics for AI-driven pipelines.Insights of Biological Databases: Biological data is the backbone of molecular biology research. This chapter discusses the structure, organization, and utilization of key databases, emphasizing data formats, redundancy issues, and retrieval systems.DNA & RNA Sequence Representation Learning Methods: Representing nucleotide sequences in ways that AI models can process effectively is a critical challenge. This chapter explores various encoding methods, from nucleotide distributions to Fourier transformations, providing a robust toolkit for researchers.Protein Sequence Representation Learning Methods: Similar to nucleic acid sequences, encoding protein sequences requires sophisticated techniques. This section details diverse methodologies, including physicochemical properties, z-scales, and context-aware encodings.CRISPR System and AI Applications: CRISPR technology has revolutionized genetic editing, and AI is accelerating its potential. This chapter examines AI-driven approaches to CRISPR-related tasks, from predictive modeling to dataset development, emphasizing the synergy between these transformative technologies.Through this volume, readers will gain a solid understanding of molecular biology and its convergence with AI. The interdisciplinary approach ensures that the biological complexities are complemented by computational rigor, laying the groundwork for the second volume, which delves deeper into advanced AI applications in molecular biology.

The integration of artificial intelligence (AI) into molecular biology has brought about a paradigm shift, enabling researchers to tackle some of the most challenging problems in life sciences. This second volume builds upon the foundational principles explored in Volume I, delving into advanced AI methodologies and their applications in understanding biological sequences at a granular level. From word embeddings to language models, this volume examines the state-of-the-art techniques driving progress in molecular biology.The chapters in this volume are structured to provide an in-depth exploration of AI methods and their transformative impact on DNA, RNA, protein, and peptide analysis:Word Embedding Methods: This chapter explores the evolution of word embedding techniques, including foundational models like Word2Vec, FastText, and GloVe, as well as advanced graph-based embeddings such as DeepWalk, Node2Vec, and Struc2Vec. These embeddings have revolutionized sequence representation, providing powerful tools for analyzing biological data.Large Language Models: Language models have reshaped the landscape of computational biology. This chapter examines models like ULMFiT, BERT, and cutting-edge tools like AlphaFold and RNAFormer, which have set new benchmarks in structure prediction and sequence analysis.AI-Driven Insights into DNA Sequence Analysis Landscape: AI has unlocked new possibilities in DNA analysis. This chapter reviews methodologies, datasets, and predictive pipelines, offering insights into the performance and distribution of research across various benchmarks.AI-Driven Insights into RNA Sequence Analysis Landscape: RNA, with its unique roles and complexities, benefits significantly from AI approaches. This chapter investigates datasets, predictive pipelines, and performance metrics specific to RNA analysis.AI-Driven Insights into Protein Sequence Analysis Landscape: Proteins, central to numerous biological processes, are analyzed using AI-driven techniques. This chapter discusses embedding-based and language model-based methods, as well as the resources and benchmarks available for protein analysis.AI-Driven Revolution in Peptide Classification Landscape: Peptides, due to their diverse biological roles, pose unique challenges. This chapter provides a thorough examination of peptide classification, exploring AI methodologies, datasets, evaluation strategies, and the state-of-the-art performance of predictive models.Volume II provides a detailed narrative of how advanced AI methodologies are transforming the study of molecular biology. Each chapter bridges the gap between theoretical advancements and practical applications, equipping researchers and practitioners with the knowledge needed to drive innovation in this interdisciplinary field.

This book presents major research and development findings achieved in the project Adaptive READ funded by the German Federal Ministry of Education and Research. Beyond techniques and systems for reading and recording documents, the emphasis of this project and the resulting book is rather on the formulation and prototype-style implementation of concepts to make document analysis systems adaptive in real-world scenarios. Furthermore, learning techniques are presented allowing for efficient content recognition and intelligent retrieval of information.

This book constitutes the refereed proceedings of the 6th International Conference on Document Analysis Systems, DAS 2004, held in Florence, Italy, in September 2004. The 31 revised full papers and 22 poster papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on digital libraries, historical documents, layout analysis, color documents, handwritten documents, graphics recognition, Internet documents, document analysis systems, and applications.

Professional knowledge management is imperative for the success of enterprises. One decisive factor for the success of knowledge management projects is the coordination of elements such as corporate culture, enterprise organization, - man resource management, as well as information and communication techn- ogy. The proper alignment and balancing of these factors are currently little understood—especially the role of information technology, which is often - garded only as an implementation tool, though it can be a catalyst by making new knowledge management solutions possible. This conference brought together representativesfrom practical and research ?elds for discussing experiences, professional applications, and visions through presentations, workshops, tutorials, and an accompanying industry exhibition. The main focus of the conference was the realization of knowledge mana- ment strategies with the aid of innovative information technology solutions, such as intelligent access to organizational memories, or integration of business processes and knowledge management. Also of interest were holistic/integrative approaches to knowledge management that deal with issues raised by the in- gration of people, organizations, and information technology.

The four-volume set LNAI 6881-LNAI 6884 constitutes the refereed proceedings of the 15th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2011, held in Kaiserslautern, Germany, in September 2011. Part 1: The total of 244 high-quality papers presented were carefully reviewed and selected from numerous submissions. The 61 papers of Part 1 are organized in topical sections on artificial neural networks, connectionists systems and evolutionary computation, machine learning and classical AI, agent, multi-agentsystems, knowledge based and expert systems, intelligent vision, image processing and signal processing, knowledge management, ontologies, and data mining.

The four-volume set LNAI 6881-LNAI 6884 constitutes the refereed proceedings of the 15th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2011, held in Kaiserslautern, Germany, in September 2011. Part 3: The total of 244 high-quality papers presented were carefully reviewed and selected from numerous submissions. The 67 papers of Part 3 are organized in topical sections on skill acquisition and ubiquitous human computer interaction, intelligent network and service, management technologies from the perspective of kansei engineering and emotion, data mining and service science for innovation, knowledge-based systems for e-business, knowledge engineering applications in process systems and plant operations, advanced design techniques for adaptive hardware and systems, human-oriented learning technology and learning support environment, design of social intelligence and creativity environment.

The four-volume set LNAI 6881-LNAI 6884 constitutes the refereed proceedings of the 15th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2011, held in Kaiserslautern, Germany, in September 2011. Part 2: The total of 244 high-quality papers presented were carefully reviewed and selected from numerous submissions. The 70 papers of Part 2 are organized in topical sections on web intelligence, text and multimedia mining and retrieval, intelligent tutoring systems and e-learning environments, other / misc. intelligent systems topics, methods and techniques of artificial and computational intelligence in economics, finance and decision making, workshop on seamless integration of semantic technologies in computer-supported office work (sistcow), innovations in chance discovery, advanced knowledge-based systems, recent trends in knowledge engineering, smart systems, and their applications.

The four-volume set LNAI 6881-LNAI 6884 constitutes the refereed proceedings of the 15th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2011, held in Kaiserslautern, Germany, in September 2011. Part 4: The total of 244 high-quality papers presented were carefully reviewed and selected from numerous submissions. The 46 papers of Part 4 are organized in topical sections on human activity support in knowledge society, knowledge-based interface systems, model-based computing for innovative engineering, document analysis and knowledge science, immunity-based systems, natural language visualisation advances in theory and application of hybrid intelligent systems.

While information and communication technology has a vast influence on our lives, little is understood about its effects on the way we learn. In the Age of Information, students – consciously or not – are learning in diverse formal and informal environments from a broad variety of sources, with scientific knowledge competing against unfounded assertions, and misinformation and biased data spreading through social and mass media. The Positive Learning in the Age of Information (PLATO) program illustrated by the contributions in this book unites outstanding and highly innovative expertise on the fundamentals of information processing and human learning to investigate a new paradigm of positive learning as a vital, morally and ethically oriented approach, which is of existential importance to maintaining the civilization standards of a modern society in the digital age.

Dieses Lehrbuch bietet eine umfassende Einführung in Grundlagen, Potentiale und Anwendungen Semantischer Technologien. Es richtet sich an Studierende der Infor¬matik und angrenzender Fächer sowie an Entwickler, die Seman¬tische Technologien am Arbeitsplatz oder in verteilten Applikationen nutzen möchten. Mit seiner an praktischen Beispielen orientierten Darstellung gibt es aber auch Anwendern und Entscheidern in Unternehmen einen breiten Überblick über Nutzen und Möglichkeiten dieser Technologie.Semantische Technologien versetzen Computer in die Lage, Informationen nicht nur zu speichern und wieder zu finden, sondern sie ihrer Bedeutung entsprechend auszuwerten, zu verbinden, zu Neuem zu verknüpfen, und so flexibel und zielgerichtet nützliche Leistungen zu erbringen.Das vorliegende Buch stellt im ersten Teil die als Semantische Technologien bezeichneten Techniken, Sprachen und Repräsentationsformalismen vor. Diese Elemente erlauben es, das in Informationen enthaltene Wissen formal und damit für den Computer verarbeitbar zu beschreiben, Konzepte und Beziehungen darzustellen und schließlich Inhalte zu erfragen, zu erschließen und in Netzen zugänglich zu machen.Der zweite Teil beschreibt, wie mit Semantischen Technologien elementare Funktionen und umfassende Dienste der Informations- und Wissensverarbeitung realisiert werden können. Hierzu gehören etwa die Annotation und das Erschließen von Information, die Suche in den resultierenden Strukturen, das Erklären von Bedeutungszusammenhängen sowie die Integration einzelner Komponenten in komplexe Ablaufprozesse und Anwendungslösungen.Der dritte Teil beschreibt schließlich vielfältige Anwendungsbeispiele in unterschiedlichen Bereichen und illustriert so Mehrwert, Potenzial und Grenzen von Semantischen Technologien. Die dargestellten Systeme reichen von Werkzeugen für persönliches, individuelles Informationsmanagement über Unterstützungsfunktionen für Gruppen bis hin zu neuen Ansätzen imInternet der Dinge und Dienste, einschließlich der Integration verschiedener Medien und Anwendungen von Medizin bis Musik.