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Computer information Sciences books have these LC Call Numbers:
Cyber Influence and Cognitive Threats by
In the wake of fresh allegations that personal data of Facebook users have been illegally used to influence the outcome of the US general election and the Brexit vote, the debate over manipulation of social Big Data continues to gain more momentum. Cyber Influence and Cognitive Threats addresses various emerging challenges in response to cybersecurity, examining cognitive applications in decision-making, behaviour and basic human interaction. The book examines the role of psychology in cybersecurity by addressing each factor involved in the process: hackers, targets, cybersecurity practitioners, and the wider social context in which these groups operate. Cyber Influence and Cognitive Threats covers a variety of topics including information systems, psychology, sociology, human resources, leadership, strategy, innovation, law, finance and others.
Designing Thriving Systems by
This monograph illuminates a design mindset for systems, artefacts, that not only survive, but thrive. Of itself an artefact is devoid of design quality - until encountered in a specific social context by human attendants.
Advances in System Reliability Engineering by
Recent Advances in System Reliability Engineering describes and evaluates the latest tools, techniques, strategies, and methods in this topic for a variety of applications. Special emphasis is put on simulation and modelling technology which is growing in influence in industry, and presents challenges as well as opportunities to reliability and systems engineers. Several manufacturing engineering applications are addressed, making this a particularly valuable reference for readers in that sector.
Sublinear Algorithms for Big Data Applications by
The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.
Python 3 and Data Analytics Pocket Primer by
As part of the best-selling Pocket Primerseries, this book is designed to introduce the reader to the basic concepts of data analytics using Python 3. Itis intended to be a fast-paced introduction to some basic features of dataanalytics and also covers statistics, data visualization, and data cleaning. Thebook includes numerous code samples using NumPy, Pandas, Matplotlib, Seaborn, and features an appendix on regularexpressions. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3 Provides a thorough introduction to data and data cleaning Covers NumPy and Pandas Introduces statistical concepts and data visualization (Matplotlib/Seaborn) Features an appendix on regular expressions Includes companion files with source code and figures
Probability Concepts and Theory for Engineers by
A thorough introduction to the fundamentals of probability theory This book offers a detailed explanation of the basic models and mathematical principles used in applying probability theory to practical problems. It gives the reader a solid foundation for formulating and solving many kinds of probability problems for deriving additional results that may be needed in order to address more challenging questions, as well as for proceeding with the study of a wide variety of more advanced topics. Great care is devoted to a clear and detailed development of the 'conceptual model' which serves as the bridge between any real-world situation and its analysis by means of the mathematics of probability. Throughout the book, this conceptual model is not lost sight of. Random variables in one and several dimensions are treated in detail, including singular random variables, transformations, characteristic functions, and sequences. Also included are special topics not covered in many probability texts, such as fuzziness, entropy, spherically symmetric random variables, and copulas. Some special features of the book are: a unique step-by-step presentation organized into 86 topical Sections, which are grouped into six Parts over 200 diagrams augment and illustrate the text, which help speed the reader's comprehension of the material short answer review questions following each Section, with an answer table provided, strengthen the reader's detailed grasp of the material contained in the Section problems associated with each Section provide practice in applying the principles discussed, and in some cases extend the scope of that material an online separate solutions manual is available for course tutors. The various features of this textbook make it possible for engineering students to become well versed in the 'machinery' of probability theory. They also make the book a useful resource for self-study by practicing engineers and researchers who need a more thorough grasp of particular topics.