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

AbstractThe use of biometrics such as fingerprints, voices, and images are becoming increasingly more ubiquitous through people’s daily lives, in applications ranging from authentication, identification, to much more sophisticated analytics, thanks to the recent rapid advances in both the sensing hardware technologies and machine learning techniques. While providing improved user experiences and better business insights, the use of biometrics has raised serious privacy concerns, due to their intrinsic sensitive nature and the accompanying high risk of leaking personally identifiable and private information.In this paper, we propose a novel utility-preserving biometric anonymization framework, which provides a method to anonymize a biometric dataset without introducing artificial or external noise, with a process that retains features relevant for downstream machine learning-based analyses to extract interesting attributes that are valuable to relevant services, businesses, and research organizations. We carried out a thorough experimental evaluation using publicly available visual and vocal datasets. Results show that our proposed framework can achieve a high level of anonymization, while at the same time retain underlying data utility such that subsequent analyses on the anonymized biometric data could still be carried out to yield satisfactory accuracy. 

Utility-Preserving Biometric Information Anonymization

IPSec is a suite of protocols that adds security to communications at the IP level. Protocols within the IPSec suite make extensive use of cryptographic algorithms. Since these algorithms are computationally very intensive, some hardware acceleration is needed to support high throughput. IPSec accelerator performance may heavily depend on the dimension of the packets to be processed. When packets are small, the time needed to transfer data and to set up the accelerator may exceed the one to process the packets (e.g. to encrypt) by software. In this paper, we propose a solution for this problem. High-level simulations and the related results are provided to show the properties of the algorithm.

Scheduling small packets in IPSec-based systems

The great majority of the adaptive nonlinear control design are based on Lyapunov's 2nd or commonly referred to as the Direct method. In the last years the “Sigmoid Generated Fixed Point Transformation (SGFPT)” has been introduced for replacing the Lyapunov technique. This systematic method has been proposed for the generation of whole families of Fixed Point Transformations. In addition it has been extended from Single Input Single Output (SISO) to Multiple Input Multiple Output (MIMO) systems. Recently, several model building issues are increasingly replaced by soft-computing based methods. In spite of the classical hard-computing methods the intelligent methodologies are able to deal with imprecisions, uncertainties, etc. by an efficient and robust way. Fuzzy logic is widely used for modeling complex and ill-defined systems. In this paper we apply the fuzzy modeling in the SGFPT control design. The applicability of the proposed scheme is confirmed by the adaptive control of the inverted pendulum system. In the investigations an “affine”, and a “soft computing-based” model were compared. Simulation results validate that the presented technique fulfills the performance criteria.

Adaptive controller using fuzzy modeling and Sigmoid Generated Fixed Point Transformation

Vorrichtung und Verfahren zur Überwachung des Laserschweissens und zum Geben eines Schweissqualitätsstatutes

This paper presents a new model for analyzing the repairability of reconfigurable system-on-chip (RSoC) instrumentation with the repair process. It exploits the connectivity of the interconnected cores in which unreliability factors due to both neighboring cores and the interconnect structure are taken into account. Based on the connectivity, two RSoC repair scheduling strategies, Minimum Number of Interconnections First (I-MIN) and Minimum Number of Neighboring Cores First (C-MIN), are proposed. Two other scheduling strategies, Maximum Number of Interconnections First (I-MAX) and Maximum Number of Neighboring cores First (C-MAX), are also introduced and analyzed to further explore the impact of connectivity-based repair scheduling on the overall repairability of RSoCs. Extensive parametric simulations demonstrate the efficiency of the proposed RSoC repair scheduling strategies; thereby manufacturing ultimately reliable RSoC instrumentation can be achieved.

/pdf/evaluating-the-repair-of-system-on-chip-soc-using-1nr83ezv3m.pdf

Vincenzo Piuri

Papers

Utility-Preserving Biometric Information Anonymization

Scheduling small packets in IPSec-based systems

Adaptive controller using fuzzy modeling and Sigmoid Generated Fixed Point Transformation

Vorrichtung und Verfahren zur Überwachung des Laserschweissens und zum Geben eines Schweissqualitätsstatutes

Evaluating the repair of system-on-chip (soc) using connectivity