Iccws20 Proceedings Of The 15th International Conference On Cyber Warfare And Security

These proceedings represent the work of contributors to the 15th International Conference on Cyber Warfare and Security (ICCWS 2020), hosted by Old Dominion University (ODU), Norfolk, Virginia on 12-13 March 2020. The Conference Chair is Prof. Brian K. Payne and the Programme Chair is Prof. Hongyi Wu, both from Old Dominion University. ICCWS is now a well-established event on the academic research calendar and now in its 15th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research. With an initial submission of 139 abstracts, after the double blind, peer review process there are 59 Academic research papers, 7 PhD research papers, 4 Masters Research papers, 1 non-academic paper and 1 work-in-progress paper published in these Conference Proceedings. These papers represent research from Australia, Canada, Czech Republic, Estonia, Finland, France, Germany, Ghana, Lesotho, Malaysia, Namibia, Nigeria, Norway, Portugal, South Africa, South Korea, Sweden, the Netherlands, and USA.

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

P.F. Strawson has supplied a new introduction for this reissue of his modern classic originally published in 1974. Subject and Predicate in Logic and Grammar explores two conceptions of subject and predicate, one of which lies at the core of standard logic and the other more closely relates to surface forms of natural language. Strawson renders these two conceptions, and their divergences, intelligible by relating them both to the 'basic case' in which the subject-term designates a substantial spatio-temporal individual. Through his treatment of these conceptions, Strawson added to our understanding of both logic and general grammar, helping us trace formal characteristics of logic and its grammar to their roots in general features of thought and experience, and observing how the grammatical structure of a large group of non-formalized languages naturally develops in various ways, along other lines. This book, based originally on seminar material used at Oxford and Princeton and a series of lectures delivered at Irvine and University College London, has become an enduring landmark in the literature of logic and the philosophy of language.

This book addresses the emerging area of cloud computing, providing a comprehensive overview of the research areas, recent work and open research problems. The move to cloud computing is no longer merely a topic of discussion; it has become a core competency that every modern business needs to embrace and excel at. It has changed the way enterprise and internet computing is viewed, and this success story is the result of the long-term efforts of computing research community around the globe. It is predicted that by 2026 more than two-thirds of all enterprises across the globe will be entirely run in cloud. These predictions have led to huge levels of funding for research and development in cloud computing and related technologies. Accordingly, universities across the globe have incorporated cloud computing and its related technologies in their curriculum, and information technology (IT) organizations are accelerating their skill-set evolution in order to be better prepared to manage emerging technologies and public expectations of the cloud, such as new services.