Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Of Theory and Practice

Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.

Body Area Networks: A Survey

It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications. Neural networks are known as universal approximators of continuous functions, but they can also approximate any mathematical operator (mapping a function to another function), which is an important capability for complex systems such as robotics control. A new deep neural network called DeepONet can lean various mathematical operators with small generalization error.

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.

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A survey on biometric cryptosystems and cancelable biometrics

2D Sine Logistic modulation map for image encryption

Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method.

Linear Discriminant Analysis Based on L1-Norm Maximization

Improving the security of 'a flexible biometrics remote user authentication scheme'

This paper presents an efficient and practical chaotic hash-based fingerprint biometric remote user authentication scheme on mobile devices e.g. cell phone and PDA. Our scheme is completely based on the new family of one-way collision free chaotic hash functions, which are much efficient than modular exponentiation-based authentication schemes e.g. RSA. Proposed scheme is two-factor authentication scheme and a user has to identify him with: something he knows (e.g. password) and something he is (e.g. fingerprint biometric). Security analysis shows that the proposed scheme provides secure, robust, and trustworthy remote authentication of mobile users over insecure network. In addition, computational costs and efficiency of the proposed scheme are encouraging for the practical implementation in the real environment.

Chaotic hash-based fingerprint biometric remote user authentication scheme on mobile devices

Although Discrete Cosine Transform (DCT) is widely employed to extract proper features for biometric recognition, the problem on how to select proper DCT coefficients to obtain the best discrimination effect has not been solved satisfactorily. Some approaches discard the low-frequency DCT coefficients unreasonably and rely on proper premasking window to improve performance. But there is not a uniform criterion to optimize the shape and size of the premasking window, so it is an inconvenient processing for coefficient selection.Three processes, used to enhance discriminant ability in DCT domain, and the relationship between them are summarized and discussed systematically. Furthermore, this paperexplains the phenomenon why the recognition rate is low without discarding the low-frequency DCT coefficients reasonably and then proposes dynamic weighted discrimination power analysis (DWDPA) to enhance the discrimination power (DP) of the selected DCT coefficients. DWDPA does not need premasking window and preserves more DCT coefficients with higher DP. Normalization prevents the DCT coefficients with large absolute values from destroying the DP of the other DCT coefficients that have less absolute values but high DP values. The DCT coefficients with larger DP values are given larger weights adaptively to optimize and enhance the recognition performance. The experiments on ORL, Yale and PolyU databases captured by biometric sensors prove the advantages of DWDPA obviously.

	 

	Key words: Dynamic weighted discrimination power analysis (DWDPA), discrete cosine transforms (DCT), biometric sensors, face recognition, palmprint recognition.

Dynamic weighted discrimination power analysis: A novel approach for face and palmprint recognition in DCT domain

http://msn.iecs.fcu.edu.tw/report/data/ori_paper/2007-6-7/2_07042622091710754.pdf

Jiashu Zhang

Papers

Linear Discriminant Analysis Based on L1-Norm Maximization

Improving the security of 'a flexible biometrics remote user authentication scheme'

Chaotic hash-based fingerprint biometric remote user authentication scheme on mobile devices

Dynamic weighted discrimination power analysis: A novel approach for face and palmprint recognition in DCT domain

Cryptanalysis and improvement on two efficient remote user authentication scheme using smart cards