Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An Introduction to MultiAgent Systems.

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

The advances and success of the Service-Oriented Architecture (SOA) paradigm have produced a revolution in ICT, particularly, in the way in which software applications are implemented and distributed. Today, applications are increasingly provisioned and consumed as web services over the Internet, and business processes are implemented by dynamically composing loosely coupled applications provided by different suppliers. In this highly dynamic context, clients (e.g., business owners or users selecting a service) are concerned about the dependability of their services and business processes. In this paper, we define a certification scheme that allows to verify the dependability properties of services and business processes. Our certification scheme relies on discrete-time Markov chains and awards machine-readable dependability certificates to services, whose validity is continuously verified using run-time monitoring. Our solution can be integrated within existing SOAs, to extend the discovery and selection process with dependability requirements and certificates, and to support a dependability-aware service composition.

Dependability certification of services: a model-based approach

The increasing demand for flexibility and scalability in dynamically obtaining and releasing computing resources in a cost-effective and device-independent manner, and ease in hosting applications without the burden of installation and maintenance, have resulted in the wide adoption of the cloud computing paradigm. Although the benefits are immense, this computing paradigm is vulnerable to a large number of system failures; as a consequence, there is increasing concern among users regarding the reliability and availability of cloud computing services. Fault tolerance and resilience serve as effective means to address users' concerns regarding reliability and availability. In this chapter, we focus on characterizing the recurrent failures in a typical cloud computing environment, analyzing the effects of failures on user applications, and surveying fault tolerance solutions corresponding to each class of failures. We also discuss the perspective of offering fault tolerance as a service to user applications as an effective means to address users' concerns regarding reliability and availability.

Chapter 9 – Fault Tolerance and Resilience in Cloud Computing Environments

The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems.

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Neural-based iterative approach for iris detection in iris recognition systems

Nowadays, the number of mobile, wearable, and embedded devices integrating sensors for acquiring cardiac signals is constantly increasing. In particular, plethysmographic (PPG) sensors are widely diffused thanks to their small form factor and limited cost. For example, PPG sensors are used for monitoring cardiac activities in automotive applications and in wearable devices as smartwatches, activity trackers, and wristbands. Recent studies focused on using PPG signals to secure mobile devices by performing biometric recognitions. Although their results are promising, all of these methods process PPG acquisitions as one-dimensional signals. In the literature, feature extraction techniques based on transformations of the spectrogram have been successfully used to increase the accuracy of signal processing techniques designed for other application scenarios. This paper presents a preliminary study on a biometric recognition approach that extracts features from different transformations of the spectrogram of PPG signals and classifies the obtained feature representations using machine learning techniques. To the best of our knowledge, this is the first study in the literature on biometric systems that extracts features from the spectrogram of PPG signals. Furthermore, with respect to most of the state-of-the-art biometric recognition techniques, the proposed approach presents the advantage of not requiring the search of fiducial points, thus reducing the computational complexity and increasing the robustness of the signal preprocessing step. We performed tests using a dataset of samples collected from 42 individuals, obtaining an average classification accuracy of \(99.16\%\) for identity verification (FMR of 0.56% at FNMR of 13.50%), and a rank-1 identification error of \(7.24\%\) for identification. The results obtained for the considered dataset are better or comparable with respect to the ones of the best-performing methods in the literature.

/pdf/biometric-recognition-of-ppg-cardiac-signals-using-rpxq8ris61.pdf

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Fault tolerance can be achieved by using time redundancy with modest hardware overhead at the expense of computation time. In this paper the REcomputing with Triplication With Voting (RETWV) technique is applied to complex arithmetic units, such as inner product units and convolvers for concurrent error correction. Hardware complexity, delay, and throughput of the RETWV concurrent error correcting inner product units are analyzed and compared. It is seen that RETWV designs can be faster than the conventional design. That is, in addition to their concurrent error correcting capability, the throughput of RETWV designs is higher than that of their nonredundant counterparts. This result is significant because this shows that the RETWV technique, which is a time redundancy approach, can be used in high performance systems.

Vincenzo Piuri

Papers

Dependability certification of services: a model-based approach

Chapter 9 – Fault Tolerance and Resilience in Cloud Computing Environments

Neural-based iterative approach for iris detection in iris recognition systems

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Efficient time redundancy for error correcting inner-product units and convolvers