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Numerical algorithms for personalized search in self-organizing information networks
\"This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks.\" The book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding web-scale data.--[book cover]
Book
Numerical algorithms for personalized search in self-organizing information networks
This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data.
Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections.
Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.
eBook
GMDH-methodology and implementation in C
Group Method of Data Handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modeling has been developed and applied to complex systems in areas like prediction, modeling, clusterization, system identification, as well as data mining and knowledge extraction technologies, to several fields including social science, science, engineering, and medicine. This book makes error-free codes available to end-users so that these codes can be used to understand the implementation of GMDH, and then create opportunities to further develop the variants of GMDH algorithms. C-language has been chosen because it is a basic language commonly taught in the first year in computer programming courses in most universities and colleges, and the compiled versions could be used for more meaningful practical applications where security is necessary.
eBook
Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities
The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.
Journal Article
Fish shoals resemble a stochastic excitable system driven by environmental perturbations
Groups of animals can perform highly coordinated collective behaviours that confer benefits to the participating individuals by facilitating social information exchange and protection from predators1. Some of these characteristics could arise when groups operate at critical points between two structurally and functionally different states, leading to maximal responsiveness to external stimuli and effective propagation of information2,3. It has been proposed that animal groups constitute examples of self-organized systems at criticality2,3; however, direct empirical evidence of this hypothesis—in particular in the wild—is mostly absent. Here we show that highly conspicuous, repetitive and rhythmic collective dive cascades produced by many thousands of freshwater fish under high predation risk resemble a stochastic excitable system driven by environmental perturbations. Together with the results of an agent-based model of the system, this suggests that these fish shoals might operate at a critical point between a state of high individual diving activity and low overall diving activity. We show that the best fitting model, which is located at a critical point, allows information about external perturbations—such as predator attacks—to propagate most effectively through the shoal. Our results suggest that criticality might be a plausible principle of distributed information processing in large animal collectives.Certain fish shoals ward off bird attacks by touching the water surface in a manner resembling waves observed in stadiums. This behaviour exhibits characteristics that suggest the fish might operate close to criticality.
Journal Article
Model Free Adaptive Control
The book summarizes theory and applications of data-driven model-free adaptive control (MFAC) which is different from the traditional adaptive control. The traditional unmodeled dynamics do not exist in MFAC framework. In addition, MFAC is suitable for many practical applications since it is easily implemented and has strong robustness. By reading this book, readers become familiar with MFAC in a short time, and can quickly carry out their independent research and applications.
eBook
Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors
The development of tracking methods for automatically quantifying individual behavior and social interactions in animal groups has open up new perspectives for building quantitative and predictive models of collective behavior. In this work, we combine extensive data analyses with a modeling approach to measure, disentangle, and reconstruct the actual functional form of interactions involved in the coordination of swimming in Rummy-nose tetra (Hemigrammus rhodostomus). This species of fish performs burst-and-coast swimming behavior that consists of sudden heading changes combined with brief accelerations followed by quasi-passive, straight decelerations. We quantify the spontaneous stochastic behavior of a fish and the interactions that govern wall avoidance and the reaction to a neighboring fish, the latter by exploiting general symmetry constraints for the interactions. In contrast with previous experimental works, we find that both attraction and alignment behaviors control the reaction of fish to a neighbor. We then exploit these results to build a model of spontaneous burst-and-coast swimming and interactions of fish, with all parameters being estimated or directly measured from experiments. This model quantitatively reproduces the key features of the motion and spatial distributions observed in experiments with a single fish and with two fish. This demonstrates the power of our method that exploits large amounts of data for disentangling and fully characterizing the interactions that govern collective behaviors in animals groups.
Journal Article
Multi-agent-based smart power management for remote health monitoring
In the Internet of things (IoT) paradigm, wireless sensor networks (WSNs) contribute hugely by connecting all the devices for smart application purposes. The smart applications include smart healthcare system, as a key phenomenon of new era of medical service. Smart healthcare system consists of different techniques and approaches, and remote health monitoring is one of those important techniques of healthcare system, which helps doctors to monitor health of remotely located patients with different health statistics provided by sensors. This whole process should be fast, prompt in response and energy efficient which is tough with limited battery power of sensors. The article proposes a novel method for efficient utilization of power in WSNs, using artificial neural network-based technique self-organizing map (SOM) for clustering and distributed artificial intelligence (DAI) for power distribution in the nodes. The hybrid approach of SOM and multi-agent-based DAI results in better performance compared to other existing methods. The performance of the proposed method is validated with mathematical analysis and simulation results, which justifies the significance of the work for IoT environment.
Journal Article
An introduction to transfer entropy : information flow in complex systems
This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series.
eBook
Embedded Intelligence on FPGA: Survey, Applications and Challenges
Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such as the need for: (1) high computational processing; (2) low power consumption (or high energy efficiency); and (3) scalability to accommodate different network sizes and topologies. In recent years, an emerging hardware technology which has demonstrated strong potential and capabilities for EI implementations is the FPGA (field programmable gate array) technology. This paper presents an overview and review of embedded intelligence on FPGA with a focus on applications, platforms and challenges. There are four main classification and thematic descriptors which are reviewed and discussed in this paper for EI: (1) EI techniques including machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map (SOM) and extreme learning; (2) applications for EI including object detection and recognition, indoor localization and surveillance monitoring, and other EI applications; (3) hardware and platforms for EI; and (4) challenges for EI. The paper aims to introduce interested researchers to this area and motivate the development of practical FPGA solutions for EI deployment.
Journal Article