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This review volume is devoted to a discussion of analogies and differences of complex production systems ? natural, as in biological cells, or man-made, as in economic systems or industrial production. Taking this unified look at production is based on two observations: Cells and many biological networks are complex production units that have evolved to solve production problems in a reliable and optimal way in a highly stochastic environment. On the other hand, industrial production is becoming increasingly complex and often hard to predict. As a result, modeling and control of such production networks involve many different spatial and temporal scales and decision policies for many different structures. The common themes of industrial and biological production include evolution and optimization, synchronization and self-organization, robust operation despite high stochasticity, and hierarchical dynamics. The mathematical techniques used come from dynamical systems theory, transport equations, control theory, pattern formation, graph theory, discrete event simulations, stochastic processes, and others. The application areas range from semiconductor production to supply chains, protein networks, slime molds, social networks, and whole economies.
This book serves as an introduction to HMC as a specific area of study within communication and to the research possibilities of HMC. The research presented here focuses on people's interactions with multiple technologies used within different contexts from a variety of epistemological and methodological approaches.
Cognitive Computing for Human-Robot Interaction: Principles and Practices explores the efforts that should ultimately enable society to take advantage of the often-heralded potential of robots to provide economical and sustainable computing applications. This book discusses each of these applications, presents working implementations, and combines coherent and original deliberative architecture for human–robot interactions (HRI). Supported by experimental results, it shows how explicit knowledge management promises to be instrumental in building richer and more natural HRI, by pushing for pervasive, human-level semantics within the robot's deliberative system for sustainable computing applications. This book will be of special interest to academics, postgraduate students, and researchers working in the area of artificial intelligence and machine learning. Key features: - Introduces several new contributions to the representation and management of humans in autonomous robotic systems; - Explores the potential of cognitive computing, robots, and HRI to generate a deeper understanding and to provide a better contribution from robots to society; - Engages with the potential repercussions of cognitive computing and HRI in the real world. - Introduces several new contributions to the representation and management of humans in an autonomous robotic system - Explores cognitive computing, robots and HRI, presenting a more in-depth understanding to make robots better for society - Gives a challenging approach to those several repercussions of cognitive computing and HRI in the actual global scenario
Providing the most comprehensive source available, this book surveys the state of the art in artificial intelligence (AI) as it relates to architecture. This book is organized in four parts: theoretical foundations, tools and techniques, AI in research, and AI in architectural practice. It provides a framework for the issues surrounding AI and offers a variety of perspectives. It contains 24 consistently illustrated contributions examining seminal work on AI from around the world, including the United States, Europe, and Asia. It articulates current theoretical and practical methods, offers critical views on tools and techniques, and suggests future directions for meaningful uses of AI technology. Architects and educators who are concerned with the advent of AI and its ramifications for the design industry will find this book an essential reference.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Digital technologies and social media have changed the processes, products, and interactions of professional communication, reshaping how, when, with whom, and where business professionals communicate. This book examines these changes by asking: How does rhetorical theory need to adapt and develop to address the changing practices of professional communication? Drawing from classical and contemporary rhetorical theory and from in-depth interviews with business professionals, the authors present a case-based approach for exploring the changing landscape of professional communication. The book develops a rhetorical theory based on networked interaction and rhetorical ethics: seeing professional communication as involving new kinds of networked interactions that require an integrated view of rhetoric and ethics. The book applies this frame to a variety of communication cases involving, for example, employee missteps on social media, corporate-consumer interactions, and the developing use of artificial intelligence agents (AI bots) to handle online communication.
This book constitutes the proceedings of the 7th International Conference on Network and System Security, NSS 2013, held in Madrid, Spain, in June 2013. The 41 full papers presented were carefully reviewed and selected from 176 submissions. The volume also includes 7 short papers and 13 industrial track papers. The paper are organized in topical sections on network security (including: modeling and evaluation; security protocols and practice; network attacks and defense) and system security (including: malware and intrusions; applications security; security algorithms and systems; cryptographic algorithms; privacy; key agreement and distribution).
System Biology Approaches for Microbial Pathogenesis Interaction Analysis aids biological researchers to expand their research scope using piled up data generated through recent technological advancement. In addition, it also opens avenues for bioinformatics and computer science researchers to utilize their expertise in biological meaningful ways. It also covers network biology approaches to decipher complex multiple host-pathogen interactions in addition to giving valuable coverage of artificial intelligence. The host-pathogen interactions are generally considered as highly specific interactions leading to a variety of consequences. The utilization of data science approaches has revolutionized scientific research including host-pathogen interaction analyses. Data science approaches coupled with network biology has taken host-pathogen interaction analysis from specific interaction to a new paradigm of understanding consequences of these interaction in the biological network. Unfortunately, basic biological researchers are mostly unaware of these advancements. In contrast, data scientists are not familiar with biological aspects of such data. System Biology Approaches for Microbial Pathogenesis Interaction Analysis will bridge these gaps through a new paradigm of understanding consequences of interaction in biological networks. • Cover approaches to decipher complex multiple host–pathogen interactions• Gives biological researcher an insight into the utilization of technological advancements in the field of host–pathogen interaction analyses in their work• Provides a new paradigm of understanding the consequences of host–pathogen interaction in biological systems
Hailed on first publication as a compendium of foundational principles and cutting-edge research, The Human-Computer Interaction Handbook has become the gold standard reference in this field. Derived from select chapters of this groundbreaking and authoritative resource, Human-Computer Interaction Fundamentals emphasizes emerging topics such as sen
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.