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Emergence -- the formation of global patterns from solely local interactions -- is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames -- phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning) -- underlie the emergence of cognition. Integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence, Downing provides a series of concrete examples of neurocognitive emergence. Doing so, he offers a new motivation for the expanded use of bio-inspired concepts in artificial intelligence (AI), in the subfield known as Bio-AI.One of Downing's central claims is that two key concepts from traditional AI, search and representation, are key to understanding emergent intelligence as well. He first offers introductory chapters on five core concepts: emergent phenomena, formal search processes, representational issues in Bio-AI, artificial neural networks (ANNs), and evolutionary algorithms (EAs). Intermediate chapters delve deeper into search, representation, and emergence in ANNs, EAs, and evolving brains. Finally, advanced chapters on evolving artificial neural networks and information-theoretic approaches to assessing emergence in neural systems synthesize earlier topics to provide some perspective, predictions, and pointers for the future of Bio-AI.

In the last years, adaptive networks have been discovered simultaneously in different fields as a universal framework for the study of self-organization phenomena. Understanding the mechanisms behind these phenomena is hoped to bring forward not only empirical disciplines such as biology, sociology, ecology, and economy, but also engineering disciplines seeking to employ controlled emergence in future technologies. This volume presents new analytical approaches, which combine tools from dynamical systems theory and statistical physics with tools from graph theory to address the principles behind adaptive self-organization. It is the first class of approaches that is applicable to continuous networks. The volume discusses the mechanisms behind three emergent phenomena that are prominently discussed in the context of biological and social sciences: synchronization, spontaneous diversification, and self-organized criticality. Self-organization in continuous adaptive networks contains extended research papers. It can serve as both, a review of recent results on adaptive self-organization as well as a tutorial of new analytical methods Self-organization in continuous adaptive networks is ideal for academic staff and master/research students in complexity and network sciences, in engineering, physics and maths.

\"This book explores adaptive security techniques through CyberAssurance for risk-based decision making in the context of software-based systems and discusses ways to achieve it. It identifies a discipline termed CyberAssurance, which considers the interactions of assurance-enhancing technology, system architecture, and the development life cycle. It looks at trust-enhancing technology in some detail, articulating a strategy based on three main prongs: building software that behaves securely (high-confidence design techniques), executing software in a protected environment (containment), and monitoring software execution for malicious behavior (detection). Applying these three prongs in combination in the proper architectural and life cycle contexts provides the best risk strategy methods for increasing our trust in software-based for Internet of Things (IoT), Cloud, and Edge systems\"-- Provided by publisher.

A unique investigation of the state of the art in design, architectures, and implementations of advanced computational infrastructures and the applications they support Emerging large-scale adaptive scientific and engineering applications are requiring an increasing amount of computing and storage resources to provide new insights into complex systems. Due to their runtime adaptivity, these applications exhibit complicated behaviors that are highly dynamic, heterogeneous, and unpredictable-and therefore require full-fledged computational infrastructure support for problem solving, runtime management, and dynamic partitioning/balancing. This book presents a comprehensive study of the design, architecture, and implementation of advanced computational infrastructures as well as the adaptive applications developed and deployed using these infrastructures from different perspectives, including system architects, software engineers, computational scientists, and application scientists. Providing insights into recent research efforts and projects, the authors include descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems. The first part of the book focuses on high-performance adaptive scientific applications and includes chapters that describe high-impact, real-world application scenarios in order to motivate the need for advanced computational engines as well as to outline their requirements. The second part identifies popular and widely used adaptive computational infrastructures. The third part focuses on the more specific partitioning and runtime management schemes underlying these computational toolkits. Presents representative problem-solving environments and infrastructures, runtime management strategies, partitioning and decomposition methods, and adaptive and dynamic applications Provides a unique collection of selected solutions and infrastructures that have significant impact with sufficient introductory materials Includes descriptions and experiences pertaining to the realistic modeling of adaptive applications on parallel and distributed systems The cross-disciplinary approach of this reference delivers a comprehensive discussion of the requirements, design challenges, underlying design philosophies, architectures, and implementation/deployment details of advanced computational infrastructures. It makes it a valuable resource for advanced courses in computational science and software/systems engineering for senior undergraduate and graduate students, as well as for computational and computer scientists, software developers, and other industry professionals.

Operating systems traditionally handle the task scheduling of one or more application instances on processor-like hardware architectures. RAMPSoC, a novel runtime adaptive multiprocessor System-on-Chip, exploits the dynamic reconfiguration on FPGAs to generate, start and terminate hardware and software tasks. The hardware tasks have to be transferred to the reconfigurable hardware via a configuration access port. The software tasks can be loaded into the local memory of the respective IP core either via the configuration access port or via the on-chip communication infrastructure (e.g. a Network-on-Chip). Recent-series of Xilinx FPGAs, such as Virtex-5, provide two Internal Configuration Access Ports, which cannot be accessed simultaneously. To prevent conflicts, the access to these ports as well as the hardware resource management needs to be controlled, e.g. by a special-purpose operating system running on an embedded processor. For that purpose and to handle the relations between temporally and spatially scheduled operations, the novel approach of an operating system is of high importance. This special purpose operating system, called CAP-OS (Configuration Access Port-Operating System), which will be presented in this paper, supports the clients using the configuration port with the services of priority-based access scheduling, hardware task mapping and resource management.

This paper describes a comparison of two Montgomery modular multiplication architectures: a systolic and a multiplexed. Both implementations target FPGA devices. The modular multiplication is employed in modular exponentiation processes, which are the most important operations of some public-key cryptographic algorithms, including the most popular of them, the RSA. The proposed systolic architecture presents a high-radix implementation with a one-dimensional array of Processing Elements. The multiplexed implementation is a new alternative and is composed of multiplier blocks in parallel with the new simplified Processing Elements, and it provides a pipelined operation mode. We compare the time × area efficiency for both architectures as well as an RSA application. The systolic implementation can run the 1024 bits RSA decryption process in just 3.23 ms, and the multiplexed architecture executes the same operation in 4.36 ms, but the second approach saves up to 28% of logical resources. These results are competitive with the state-of-the-art performance.

Kahn process networks (KPNs) is a distributed model of computation used for describing systems where streams of data are transformed by processes executing in sequence or parallel. Autonomous processes communicate through unbounded FIFO channels in absence of a global scheduler. In this work, we propose a task-aware middleware concept that allows adaptivity in KPN implemented over a Network on Chip (NoC). We also list our ideas on the development of a simulation platform as an initial step towards creating fault tolerance strategies for KPNs applications running on NoCs. In doing that, we extend our SACRE (Self-Adaptive Component Run Time Environment) framework by integrating it with an open source NoC simulator, Noxim. We evaluate the overhead that the middleware brings to the the total execution time and to the total amount of data transferred in the NoC. With this work, we also provide a methodology that can help in identifying the requirements and implementing fault tolerance and adaptivity support on real platforms.