In this paper, a hybrid anomaly intrusion detection scheme using program system calls is proposed. In this scheme, a hidden Markov model (HMM) detection engine and a normal database detection engine have been combined to utilise their respective advantages. A fuzzy-based inference mechanism is used to infer a soft boundary between anomalous and normal behaviour, which is otherwise very difficul...

The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently led to a resurgence in the use of Artificial Neural Network Hidden Markov Model (ANN/HMM) hybrid systems for Automatic Speech Recognition (ASR). In this paper we report results of a DBN-pretrained context-dependent ANN/HMM system trained on two datasets that are much larger than any reported previously with DBN-pretr...

Although HMM is widely used for on-line handwriting recognition, there is no simple and wellestablished method of designing the HMM topology. We propose a data-driven systematic method to design HMM topology. Data samples in a single pattern class are structurally simplified into a sequence of straight-line segments, and then these simplified representations of the samples are clustered. An HMM...

As recently introduced in [1], an HMM2 can be considered as a particular case of an HMM mixture in which the HMM emission probabilities (usually estimated through Gaussian mixtures or an artificial neural network) are modeled by statedependent, feature-based HMM (referred to as frequency HMM). A general EM training algorithm for such a structure has been developed [2]. Although there are numero...

Hexamethylmelamine (HMM) is metabolized by rat hepatic microsomal preparat ions to reactive species which covalent ly bind to microsomal protein and to calf thymus DNA added to microsomal incubation mixtures. Covalent binding to macromolecules is dependent on the presence of molecular oxygen and reduced nicot inamide adenine dinucleot ide phosphate and is catalyzed by cytochrome P-450 monooxyge...

This research seeks to generate temporal event predictions using the sticky Hierarchical Dirichlet Process Hidden Markov Model (sticky HDP-HMM) [2], a generalization of the infinite HMM [1]. Hidden Markov Models (HMMs) are one of the most widely used machine learning techniques for analyzing temporal data. One significant limitation of this traditional approach is that the number of states in t...

This paper reports on a large vocabulary speaker independent isolated word recognizer targeting 50,000 words. The system supports a unique four-layer sharing structure for either continuous HMM or discrete HMM. Evaluation is performed using a dictionary of 5000 US city names, a dictionary of the 5000 English most frequent words, a dictionary of 50,000 English words, and the 110,000 word CMU Eng...

• Two types of multiscale and monolithic ALE-FEM are developed for the hemodynamic FSI problem that involves aneurysm progression. The heterogeneous method (HMM) seamless (SMM) integrated into ALE-FEM. can predict long-term risk within a short time cardiovascular biological simulations. is numerically validated by 2D example in terms its approximation accuracy. HMM/ALE-FEM applied to AAA patien...