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This book comprises the select peer-reviewed proceedings of the 3rd International Conference on Information Technology (InCITe-2023). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in decision intelligence, deep learning, machine learning, artificial intelligence, data science, and enabling technologies for IoT, blockchain, and other futuristic computational technologies. It covers various topics that span cutting-edge, collaborative technologies and areas of computation. The content would serve as a rich knowledge repository on information & communication technologies, neural networks, fuzzy systems, natural language processing, data mining & warehousing, big data analytics, cloud computing, security, social networks, and intelligence, decision-making, and modeling, information systems, and IT architectures. This book provides a valuable resource for those in academia and industry.
In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images.The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends.This must-have volume is a relevant reference work for librarians, archivists and computer scientists.
Charting the evolution of practicing digital history Historians have seen their field transformed by the digital age. Research agendas, teaching and learning, scholarly communication, the nature of the archive—all have undergone a sea change that in and of itself constitutes a fascinating digital history. Yet technology's role in the field's development remains a glaring blind spot among digital scholars. Adam Crymble mines private and web archives, social media, and oral histories to show how technology and historians have come together. Using case studies, Crymble merges histories and philosophies of the field, separating issues relevant to historians from activities in the broader digital humanities movement. Key themes include the origin myths of digital historical research; a history of mass digitization of sources; how technology influenced changes in the curriculum; a portrait of the self-learning system that trains historians and the problems with that system; how blogs became a part of outreach and academic writing; and a roadmap for the continuing study of history in the digital era.
This book comprises the proceedings of the International Conference on Intelligent Systems and Applications (ICISA 2022). The contents of this volume focus on novel and modified artificial intelligence and machine learning-based methods and their applications in robotics, pharmaceutics, banking & finance, agriculture, food processing, crime prevention, smart homes, transportation, traffic control, and wildlife conservation, etc. This volume will prove a valuable resource for those in academia and industry.
As a vibrant area of computer science which continues to develop rapidly, AI is a field in which fresh ideas and new perspectives are of particular interest. This book presents the proceedings of the 8th European Starting AI Researcher Symposium (STAIRS 2016), held as a satellite event of the 22nd European Conference on Artificial Intelligence (ECAI) in The Hague, the Netherlands, in August 2016. What is unique about the STAIRS symposium is that the principal author of every submitted paper must be a young researcher who either does not yet hold a Ph.D., or who has obtained their Ph.D. during the year before the submission deadline for papers. The book contains 21 accepted papers; Part I includes the 11 long papers which were presented orally at the symposium, and Part II the remaining long and short papers presented in poster sessions. These papers cover the entire field of AI, with social intelligence and socio-cognitive systems, machine learning and data mining, autonomous agents and multiagent systems, being the areas which attracted the largest number of submissions. There is a good balance between foundational issues and AI applications, and the problems tackled range widely from classical AI themes such as planning and scheduling or natural language processing, to questions related to decision theory and games, as well as to other newly emerging areas. Providing a tantalizing glimpse of the work of AI researchers of the future, the book will be of interest to all those wishing to keep abreast of this exciting and fascinating field.
This volume approaches an understanding of the term versioning in the broadest sense, discussing ideas about how versions differ across forms of media, including text, image, and sound. Versions of cultural objects are identified, defined, articulated, and analysed through diverse mechanisms in different fields of research. The study of versions allows for the investigation of the creative processes behind the conception of works, a closer inspection of their socio-political contexts, and promotes investigation of their provenance and circulation. Chapters in this volume include discussion of what a "version" means in different fields, case studies implementing digital versioning techniques, conceptual models for representing versions digitally, and computational and management issues for digital projects.
Historical maps are fascinating documents and a valuable source of information for scientists of various disciplines. Many of these maps are available as scanned bitmap images, but in order to make them searchable in useful ways, a structured representation of the contained information is desirable. This book deals with the extraction of spatial information from historical maps. This cannot be expected to be solved fully automatically (since it involves difficult semantics), but is also too tedious to be done manually at scale. The methodology used in this book combines the strengths of both computers and humans: it describes efficient algorithms to largely automate information extraction tasks and pairs these algorithms with smart user interactions to handle what is not understood by the algorithm. The effectiveness of this approach is shown for various kinds of spatial documents from the 16th to the early 20th century.
This book presents valuable contributions devoted to practical applications of Machine Intelligence and Big Data in various branches of the industry. All the contributions are extended versions of presentations delivered at the Industrial Session the 6th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2015) held in Warsaw, Poland at June 30- July 3, 2015, which passed through a rigorous reviewing process. The contributions address real world problems and show innovative solutions used to solve them. This volume will serve as a bridge between researchers and practitioners, as well as between different industry branches, which can benefit from sharing ideas and results.“/p>
In traditional service architectures that follow the service statelessness principle, the state is primarily held in the data tier. Here, service operators utilize tailored storage solutions to guarantee the required availability; even though failures can occur at any time. This centralized approach to store and process an application’s state in the data tier implies that outages of the entire tier cannot be tolerated. An alternative approach, which is in focus of this thesis, is to decentralize the processing of state information and to use more stateful components in the early tiers. The possibility to tolerate a temporary outage of an entire tier implies that the application’s state can be manipulated by the remaining tiers without waiting for approval from the unavailable tier. This setup requires multi-leader replication, where every replica can accept writes and forwards the resulting changes to the other replicas. This thesis explores the feasibility of using multi-leader replication to store and process state in a decentralized manner across multiple tiers. To this end, two replication mechanisms, namely Conflict-free Replicated Data Types and Operational Transformation, are under particular investigation. We use and extend both mechanism to demonstrate that the aforementioned decentralization is worth considering when designing a service architecture. The challenges that arise when following our approach go back to fundamental impossibility results in distributed systems research, i.e. the impossibility to achieve a fault-tolerant consensus mechanism in asynchronous systems and the inevitable trade-off between availability and consistency in the presence of failures. With this thesis, we contribute to close the exposed gaps of both results by providing usable alternatives for standard IT services. We exemplify the feasibility of our alternatives with a fully distributed IMAP service and a programming library that provides the necessary extension to utilize our approach in a variety of web-based applications. All contributions of this thesis are based on both theory and practice. In particular, all extensions to the existing multi-leader replication mechanisms were proven to satisfy the necessary properties. Moreover, those extensions were also implemented as prototypical applications and evaluated against the corresponding de facto standard software from the industry. Basierend auf dem “service statelessness principle” ist es üblich, Softwaredienste so zu entwerfen, dass der Zustand des Dienstes primär in einer gekapselten Datenschicht verarbeitet wird. Innerhalb der Datenschicht werden spezielle Lösungen verwendet, um die Verfügbarkeit der Daten sicherzustellen. Dieser zentralisierte Ansatz hat zur Folge, dass ein Ausfall oder eine temporäre Nichtverfügbarkeit der gesamten Datenschicht zwangsweise zur Nichtverfügbarkeit des gesamten Dienstes führt. Ein alternativer Ansatz, welcher in dieser Arbeit erforscht wird, ist die dezentralisierte Speicherung und Verarbeitung der Daten in den darüberliegenden Softwareschichten. Um in diesem Ansatz einen Ausfall der gesamten Datenschicht zu kompensieren, ist es zwingend notwendig, dass die verbleibenden Schichten die eingehenden Anfragen ohne die Bestätigung durch die Datenschicht beantworten können. Hierfür wird eine Replikationsarchitektur benötigt, in der jedes Replikat die Anfragen direkt beantworten kann; die so genannte “multi-leader replication”. In dieser Arbeit werden diese Replikationsarchitekturen verwendet, um den Zustand und die Daten eines Dienstes zu dezentralisieren und über mehrere Schichten zu replizieren. Hierbei werden zwei Mechanismen detaillierter betrachtet: “Conflict-free Replicated Data Types” und “Operational Transformation”. Anschließend werden beide Mechanismen erweitert und hinsichtlich der Verwendbarkeit für den beschriebenen Ansatz geprüft. Als Ergebnis dieser Arbeit wird gezeigt, dass ein dezentralisierter Ansatz mit den vorgestellten Mechanismen in Betracht gezogen werden kann. Die Herausforderungen, die bei der Anwendung dieses Ansatzes entstehen, basieren auf nachweislich unlösbaren Problemen aus der Forschung von Verteilten Systemen. Dazu gehört die Unlösbarkeit von Konsensus und die unausweichliche Abwägung zwischen Verfügbarkeit und Konsistenz in einem verteilten System mit Ausfällen. Diese Arbeit trägt dazu bei, die entstehenden Lücken, welche aus diesen fundamentalen Ergebnissen resultieren, zu schließen und die vorgeschlagenen Lösungen für reale IT Dienste anwendbar zu machen. Dieses wird anhand eines dezentralen IMAP Dienstes und einer Programmierbibliothek für Webanwendungen verdeutlicht. Alle Bestandteile dieser Doktorarbeit verbinden Theorie und Praxis. Alle vorgeschlagenen Erweiterungen für bestehende Replikationssysteme werden in formalen Modellen verifiziert und prototypisch implementiert. Die Implementierungen werden außerdem mit vergleichbarer Standardsoftware, welche dem heutigen Stand der Technik entspricht, in praktischen Experimenten evaluiert.
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. Investigates various methodologies and algorithms for data summarization, clustering and classification Covers both theory and practical applications from around the world, across all related disciplines of Intelligent Information Filtering and Organization Systems Explores different challenges and issues related to spam filtering, attribute selection, and classification for large datasets