On Wang $k$ WTA With Input Noise, Output Node Stochastic, and Recurrent State Noise.

In this paper, the effect of input noise, output node stochastic, and recurrent state noise on the Wang $k$ WTA is analyzed. Here, we assume that noise exists at the recurrent state $y(t)$ and it can either be additive or multiplicative. Besides, its dynamical change (i.

A HIPAA-compliant key management scheme with revocation of authorization.

Patient control over electronic protected health information (ePHI) is one of the major concerns in the Health Insurance and Accountability Act (HIPAA). In this paper, a new key management scheme is proposed to facilitate control by providing two functionalities. First, a patient can authorize more than one healthcare institute within a designated time period to access his or her ePHIs.

On-line node fault injection training algorithm for MLP networks: objective function and convergence analysis.

Improving fault tolerance of a neural network has been studied for more than two decades. Various training algorithms have been proposed in sequel. The on-line node fault injection-based algorithm is one of these algorithms, in which hidden nodes randomly output zeros during training.

A novel key management solution for reinforcing compliance with HIPAA privacy/security regulations.

Digitizing medical records facilitates the healthcare process. However, it can also cause serious security and privacy problems, which are the major concern in the Health Insurance Portability and Accountability Act (HIPAA). While various conventional encryption mechanisms can solve some aspects of these problems, they cannot address the illegal distribution of decrypted medical images, which violates the regulations defined in the HIPAA.

Convergence and objective functions of some fault/noise-injection-based online learning algorithms for RBF networks.

In the last two decades, many online fault/noise injection algorithms have been developed to attain a fault tolerant neural network. However, not much theoretical works related to their convergence and objective functions have been reported. This paper studies six common fault/noise-injection-based online learning algorithms for radial basis function (RBF) networks, namely 1) injecting additive input noise, 2) injecting additive/multiplicative weight noise, 3) injecting multiplicative node noise, 4) injecting multiweight fault (random disconnection of weights), 5) injecting multinode fault during training, and 6) weight decay with injecting multinode fault.

On objective function, regularizer, and prediction error of a learning algorithm for dealing with multiplicative weight noise.

In this paper, an objective function for training a functional link network to tolerate multiplicative weight noise is presented. Basically, the objective function is similar in form to other regularizer-based functions that consist of a mean square training error term and a regularizer term. Our study shows that under some mild conditions the derived regularizer is essentially the same as a weight decay regularizer.