Dueling-HMM Analysis on Masquerade Detection

We present a path planning framework that takes into account the human's safety perception in the presence of a flying robot. The framework addresses two objectives: (i) estimation of the uncertain parameters of the proposed safety perception model based on test data collected using Virtual Reality (VR) testbed, and (ii) offline optimal control computation using the estimated safety perception model. Due to the unknown factors in the human tests data, it is not suitable to use standard regression techniques that minimize the mean squared error (MSE). We propose to use a Hidden Markov model (HMM) approach where human's attention is considered as a hidden state to infer whether the data samples are relevant to learn the safety perception model. The HMM approach improved log-likelihood over the standard least squares solution. For path planning, we use Bernstein polynomials for discretization, as the resulting path remains within the convex hull of the control points, providing guarantees for deconfliction with obstacles at low computational cost. An example of optimal trajectory generation using the learned human model is presented. The optimal trajectory generated using the proposed model results in reasonable safety distance from the human. In contrast, the paths generated using the standard regression model have undesirable shapes due to overfitting. The example demonstrates that the HMM approach has robustness to the unknown factors compared to the standard MSE model.

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

In the past years, Hidden Markov Models have been used in several fields and applications with success. More recently, these models have been applied to improve the reliability of a machinery system. In many cases, failure is preceded by specific sequences of events (signature), which can be detected by an adequate Hidden Markov Model (HMM). Classical laws like lifetime models or survival functions are used to estimate the lifetime of a system. The default of these laws is that only the elapsed time is used to estimate the end of life of a system. The aim of this paper is to validate an HMM approach. We first use a synthetic HMM model of degradation to produce event sequences. This synthetic model has been inspired by a real process. In this case, we can adjust the failure rate by changing model parameters. All the parameters of this synthetic model are known and provide references which can be evaluated by different indicators. Classical survival functions used in reliability are computed on synthetic sequences. These laws validate the behavior of the synthetic model. The higher the failure rate, the shorter the lifetime duration. These results confirm that a four-state, left to right, HMM topology can represent the degradation level of a system. In a second time, this HMM approach is used in a real case, where degradation levels are unknown. Degradation estimates are compared with the results from classical survival functions used in the first case. Then we show that the degradation level provided by the HMM approach is more efficient than the survival functions approach. The HMM approach takes into account the events collected about a system, not only the elapsed time as is the case with survival functions.

This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM). HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD). Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.

Masquerade attacks are attempts by unauthorized users to gain access to confidential data or greater access privileges, while pretending to be legitimate users. Detection of masquerade attacks is of great importance and is a non-trivial task of system security. While several approaches do exist for masquerade detection, the relative effectiveness of approaches still needs considerable improvement. While in the past certain cost formulations have been used to compute the overall performance of masquerade detection methods, but these formulations appeared to be biased. Hence we present a formulation to compute the effectiveness of masquerade detection and also present a highly effective approach to masquerade detection using Hidden Markov Models (HMM). Our experimentation is on the well-known Schonalu dataset (SEA). Experimentation shows our approach to be most effective in the set of known approaches.

In this chapter we introduce an original approach to predict a day-ahead energy demand, based on machine learning and pattern recognition. Our Hidden Markov Model (HMM) is a simple and explainable model that uses integer sequences to define emission probability distributions attached to states. We develop a Mathematica code which relies on the maximum likelihood principle in HMM environment. Based on these exploratory results, we conclude that the HMM approach is an efficient way in modeling short-term/day-ahead energy demand prediction, especially during peak period(s) and in accounting for the inherent stochastic nature of demand conditions. The model can be easily extended to predict energy demand values for more than one day in the future. However, the accuracy of such predictions would decrease as we expect.

A masquerade attack, in which an attacker impersonates a legitimate user to utilize the user's privileges, can be triggered either by someone within the organization or by an outsider. We propose the sequence alignment based hidden Markov model (SA-HMM) approach, where we incorporate the benefits of both the sequence alignment and continuous hidden Markov model (HMM). The sequence alignment module for the proposed algorithm allows the algorithm to tolerate variations in user activity sequence. The HMM module takes the positional information between the observations of users into account. The proposed approach achieves a high hit ratio of 94.1% outperforming existing masquerade detection approaches.

Sleep quantity affects an individual's personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive and cause discomfort besides being expensive. In this work, we present an algorithm to detect sleep using a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states - Sleep and Wake. In the first approach, we take an equation from Kushida's algorithm to process accelerometer data. Henceforth, we call it Kushida's equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) training. Although all the three approaches are suitable for a phone's resources, each approach demands different amount of resources. While Kushida's equation-based approach demands the least, the HMM training-based approach demands the maximum. We collected data from mobile phone's accelerometer for four subjects for twelve days each. We compare accuracy of sleep detection using each of the three approaches with that of Zeo sensor, which is based on Electroencephalogram (EEG) sensor to detect sleep. EEG is an important modality in PSG. We find that HMM training-based approach is as much as 84% accurate. It is 15% more accurate as compared to Kushida's equation-based approach and 10% more accurate as compared to statistical method-based approach. In order to concisely represent the sleep quality of people, we model their sleep data using HMM. We present an analysis to find out a tradeoff between the amount of training data and the accuracy provided in the modeling of sleep. We find that six days of sleep data is sufficient for accurate modeling. We compare accuracy of our HMM training-based algorithm with a representative third party app SleepTime available from Google Play Store for Android. We find that the detection done using HMM approach is closer to that done by Zeo by 13% as compared to the third party Android application SleepTime. We show that our HMM training-based approach is efficient as it takes less than ten seconds to get executed on Moto G Android phone.

A Hidden Markov Model to Detect On-Shelf Out-of-Stocks Using Point of Sales Data

A continuous-parameter hidden Markov model (HMM) is proposed and compared with a discrete-parameter HMM, a time delay neural network (TDNN), and LVQ2 by using the same training and testing database. It is found that the proposed model's performance is comparable to that of TDNN or LVQ2. Higher performance (96 approximately 97%) is obtained for all Japanese phonemes in isolated words. The HMM approach is superior to others for the recognition of time-sequential patterns like continuous speech.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, a novel recursive data mining method based on the simple but powerful model of cognition called a conceptor is introduced and applied to computer security. The method recursively mines a string of symbols by finding frequent patterns, encoding them with unique symbols and rewriting the string using this new coding. We apply this technique to two related but important problems in computer security: (i) masquerade detection to prevent a security attack in which an intruder impersonates a legitimate user to gain access to the resources, and (ii) author identification, in which anonymous or disputed computer session needs to be attributed to one of a set of potential authors. Many methods based on automata theory, hidden Markov models, Bayesian models or even matching algorithms from bioinformatics have been proposed to solve the masquerading detection problem but less work has been done on the author identification. We used recursive data mining to characterize the structure and high-level symbols in user signatures and the monitored sessions. We used one-class SVM to measure the similarity of these two characterizations. We applied weighting prediction scheme to author identification. On the SEA dataset that we used in our experiments, the results were very promising.

An experiment is described to investigate the application of hidden Markov modeling (HMM) to QRS detection. Previously reported detection algorithms employ decision rule stages which require specification and adjustment of numerous parameters for adequate detection accuracy. The hidden Markov modeling approach provides automatic estimation of all parameters in the decision rule stage from each ECG file undergoing analysis. Preliminary results using a simple HMM detector show performance comparable to that of a multiparameter threshold based decision rule scheme.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Systems and methods for automatically segmenting speech inventories. A set of Hidden Markov Models (HMMs) are initialized using bootstrap data. The HMMs are next re-estimated and aligned to produce phone labels. The phone boundaries of the phone labels are then corrected using spectral boundary correction. Optionally, this process of using the spectral-boundary-corrected phone labels as input instead of the bootstrap data is performed iteratively in order to further reduce mismatches between manual labels and phone labels assigned by the HMM approach.

BackgroundMigraine is a common neurological disorder involving network-level dysfunction. Increasing evidence suggests that migraine involves network-level dysfunction and is associated with altered resting-state functional connectivity. Traditional static functional connectivity analyses are limited in capturing the temporal dynamics of large-scale brain networks. The Hidden Markov Model (HMM) provides an advanced analytical framework to identify discrete, recurrent brain states and characterize their temporal properties without the constraints of arbitrary windowing assumptions.ObjectiveTo characterize dynamic functional connectivity alterations in young patients with migraine without aura (MWoA) using HMM and examine associations between dynamic state metrics and clinical disability.MethodsResting-state fMRI data were obtained from 200 participants (100 young MWoA patients and 100 matched healthy controls). Using the Dosenbach 160 ROI template (cerebellum excluded; N = 142), HMM identified recurring brain states. Group differences in fractional occupancy (FO), mean dwell time (MDT), and state transition probabilities were assessed. State-specific functional connectivity patterns were compared, and correlations with clinical indices (MIDAS, VAS, HIT-6) were evaluated.ResultsEleven robust dynamic brain states were identified. Compared with controls, migraine patients demonstrated increased FO and MDT in States 7 (dorsal attention network–dominant) and 9 (multisensory integration), alongside reduced values in sensorimotor states (States 3, 4, 8, 11). State 9 exhibited significant abnormalities in DMN–SC and DMN–VAN connectivity (FDR-corrected q < 0.05). Transition analyses revealed enhanced self-transitions and increased incoming transitions to States 7 and 9, whereas transitions among sensorimotor states were diminished. MDT in State 9 was positively correlated with MIDAS scores (r = 0.38, p < 0.05), indicating its association with functional disability.ConclusionsYoung MWoA patients exhibit a dual-mode dysfunction in brain dynamics: heightened external vigilance (State 7) and impaired segregation of internal–external processing (State 9), accompanied by instability in baseline sensorimotor configurations. Prolonged dwelling in State 9 and its correlation with disability highlight this multisensory integration state as a potential biomarker of migraine-related functional impairment. These findings provide new insights into neurobiological mechanisms and support dynamic network–based therapeutic strategies.

This paper narrates a system for face recognition based on pseudo 2D hidden Markov model (HMM) and discrete cosine transformation (DCT) coding. A sequence of overlapping sub-images, constituting the observation windows, is extracted from each face image, which is followed by computation of the DCT coefficients for each of them. The whole sequence is then modelled using HMM. Selection of the model parameters has been optimized for software execution speed and performance accuracy through intensive experimentation on facial data captured on the setup in our lab, which has been made reasonably constrained in order to reduce the data variations. HMM methodology provides robustness against the inadvertent and unavoidable variations occurring in the facial image data of a subject acquired from instant to instant. The results show that our lab system has attained a peak face recognition success rate of 99.1% after optimization of model parameters. The novelty of our matching technique lies in the inclusion of the rejection-HMM model in the system reference database, which prevents the system from responding to query images of registered subjects with large variations, as well as to those of subjects not registered with the system. This improves the recognition performance by reducing false acceptance rate even at the cost of increasing false rejection rate marginally, and makes the technique suitable for verification mode of operation. The system finds application in facial database search, access control, and the like. (4 pages)