ZipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

A network intrusion can be any unauthorized activity on a network and network intrusion detection is a significant topic in cybersecurity. Data analytics is conducted on the database ‘spambase’ as an example of analysis for network intrusion detection based on the Naïve Bayesian classification, deep learning with the algorithm of Rprop+ and the hidden Markov model (HMM), respectively. All the analysis is fulfilled using R language and its functions. An HMM based on the Baum–Welch algorithm has been created on the database ‘spambase’ through training and parameter estimation. An HMM-based spam-email prediction has been performed through the probability evaluation based on the forward algorithm. The analytics results obtained from the above three methods are compared. It is shown that HMM-based analytics can achieve the best accuracy in the spam-email classification although only a few features are used in the HMM while all features are used in the Naïve Bayesian classification and deep learning.

A new text categorization algorithm based on Hidden Markov Model is proposed. At first, semantic clusters are obtained from training data set. The association between semantic clusters is modeled as Hidden Markov Model. Combining with the forward algorithm, the strategy could realize automatic text categorization. From the simulation, the proposed text categorization algorithm is better in categorization precision. Moreover, it works well independent of the number of considered categories compared to the priori art algorithms.Keywordstext categorizationHidden Markov modelsSemantic Clustertext serialization

The study reported in this paper represents a continuation of our previous work on the application of Hidden Markov models (HMMs) to the translational and rotational invariant classification of SAR targets. The traditional method of making classification decisions using an HMM does not achieve the desired objective of minimizing the number of misclassifications. We present a novel technique that minimizes the probability of misclassification error. This approach, which is an adaptation of an existing Minimum Classification Error strategy, is globally optimal. The proposed method applies basic principles of pattern recognition to reduce the expected misclassification rate by dynamically perturbing the HMM parameters using a constraint on a cross-entropy measure and distance separation between pairs of HMM models. Like the traditional implementation of an HMM, our new formulation can still be implemented using an efficient forward-backward algorithm for estimating the HMM parameters. We tested our classifier on a public mixed- target MSTAR database and compared our approach to the original HMM approach trained by using a maximum likelihood criterion. The results indicate a significant improvement over the original HMM approach. Current scores from our method are about in excess of 90% on testing data sets.

Real-time collection of athletes’ abnormal training data can improve the training effect of athletes. This paper studies the real-time collection method of athletes’ abnormal training data based on machine learning. The main motivation of this paper is to collect the athletes’ abnormal training data in time, which can help to evaluate and improve the training effect. Four sensor nodes are arranged in the upper and lower limbs of athletes to collect the angular velocity, acceleration, and magnetic field strength data of athletes in training state. The data are sent to the data transmission base station through wireless sensors, and the data transmission base station transmits the data to the data processing terminal. The data processing terminal calculates the difference between the sample values of each sensor to obtain the data dispersion of each sensor. The features of each dimension data in a time domain and frequency domain are obtained by using the dispersion degree to construct 32-dimensional feature vectors, and the extracted feature vectors are input into the hidden Markov model. The forward algorithm is used to obtain the probability of the final observation sequence, so as to realize the final collection of athletes’ abnormal training data. The experimental results show that the accuracy and recall rate of the abnormal data collected by this method is higher than 98%, which requires less time.

Önemli yatırım araçlarından biri olan altın, piyasalar üzerindeki etkisi sebebiyle dünya çapında birçok yatırımcı tarafından takip edilmektedir. Bu sebeple altın fiyatlarının tahmini ve etkileyen faktörlerin belirlenmesi önemli problemlerden biridir. Bu çalışmada, altın fiyatlarındaki yüzdesel değişim oranı ve sebepleri saklı Markov modelleri ile tahmin edilmiştir. Bunun için 2013-2023 yılları arası altın, dolar, faiz ve enflasyon oranı verileri kullanılmıştır. Altın fiyatlarındaki yüzdesel değişim gözlemler olarak tanımlanırken, altın fiyatlarını etkileyen dolar, faiz ve enflasyon oranlarından mümkün 18 saklı durum elde edilmiştir. Saklı Markov modeli birinci temel probleminin çözümünde ileri yön algoritması kullanılmıştır. Bu algoritma ile 2 aylık dönemlerde karşılaşılacak gözlem dizisi olasılıkları hesaplanmıştır. Bu dönemde altın fiyatlarındaki yüzdesel değişimin 0,5’ten büyük olması olasılığı %23,6 olarak bulunmuştur. Gözlem dizilerini en iyi açıklayan saklı durumlar Viterbi algoritması ile incelenmiştir. Elde edilen sonuçlar gerçek veriler ile karşılaştırdığında yüksek doğruluk oranlarının elde edildiği gözlenmiştir.

Hidden Markov models (HMM) have been proposed as a method of analysis for noisy single-molecule fluorescence resonance energy transfer (SM FRET) data. However, there are practical and fundamental limits in applying HMM to SM FRET data due to the short photobleaching lifetimes of fluorophores and the limited time resolution of detection devices. The fast photobleaching fluorophores yield short SM FRET time traces, and the limited detection time resolution generates abnormal FRET values, which result in systematic underestimation of kinetic rates. In this work, a HMM algorithm is implemented to optimize one set of HMM parameters with multiple short SM FRET traces. The FRET efficiency distribution function for the HMM optimization was modified to accommodate the abnormal FRET values resulting from limited detection time resolution. Computer simulations reveal that one set of HMM parameters is optimized successfully using multiple short SM FRET traces and that the degree of the kinetic rate underestimation is reduced by using the proposed modified FRET efficiency distribution. In conclusion, it is demonstrated that HMM can be used to reproducibly analyze short SM FRET time traces.

Hidden Markov model (HMM) based recognition of handwriting is now quite common, but the incorporation of HMM's into a complex stochastic language model for handwriting recognition is still in its infancy. We have taken advantage of developments in the speech processing field to build a more sophisticated handwriting recognition system. The pattern elements of the handwriting model are subcharacter stroke types modeled by HMMs. These HMMs are concatenated to form letter models, which are further embedded in a stochastic language model. In addition to better language modeling, we introduce new handwriting recognition features of various kinds. Some of these features have invariance properties, and some are segmental, covering a larger region of the input pattern. We have achieved a writer independent recognition rate of 94.5% on 3,823 unconstrained handwritten word samples from 18 writers covering a 32 word vocabulary.

Hidden Markov models with set-valued parameters

In this paper, the audio and visual features of speech are integrated using a novel fused-HMM. We assume that the two sets of features may have different data rates and duration. Hidden Markov models (HMMs) are first used to model them separately, and then a general Bayesian fusion method, which is optimal in the maximum entropy sense, is employed to fuse them together. Particularly, an efficient learning algorithm is introduced. Instead of maximizing the joint likelihood of the fuse-HMM, the learning algorithm maximizes the two HMMs separately, and then fuses the HMMs together. In addition, an inference algorithm is proposed. We have tested the proposed method by person verification experiments. Results show that the proposed method significantly reduces the recognition error rates as compared to the unimodal HMMs and the loosely-coupled fusion model.

Distribution network operators are becoming increasingly interested in accurately anticipating load characteristics at the low voltage level. Energy disaggregation could be one of the potential approaches to exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modelling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward backward algorithm, which is based on the framework of expectation maximization (EM), is formalized for the HHMM fitting process. Tests on publicly available data show that the HHMM and proposed algorithm can effectively handle the modelling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation.

In this paper we show how privacy of genomic sequences can be protected while they are analyzed using Hidden Markov Models (HMM), which is commonly done in bioinformatics to detect certain non-beneficial patterns in the genome. Besides offering strong privacy guarantees, our solution also allows protecting the intellectual property of the parties involved, which makes the solution viable for implementation of secure bioinformatics services. In particular, we show how two mutually mistrusting parties can obliviously run the forward algorithm in a setup where one party knows a HMM and another party knows a genomic string; while the parties learn whether the model fits the genome, they neither have to disclose the parameterization of the model nor the sequence to each other. Despite the huge number of arithmetic operations required to solve the problem, we experimentally show that HMMs with sizes of practical importance can obliviously be evaluated using computational resources typically found in medical laboratories. As a central technical contribution, we give improved protocols for secure and numerically stable computations on non-integer values.

Position estimation in localization and tracking is often approached with Bayesian filtering. In combination with Gaussian assumptions about state distributions this approach results in efficient algorithms such as the Kalman filter. Indoors, however, the location state space is naturally constrained by walls and other obstacles that cannot be taken into account with Kalman or other Gaussian filters. More general Hidden Markov Models (HMMs) on the other hand do not make restrictive assumptions and thus allow for extensive explicit modelling. In particular for indoor positioning, we show how to inject geometric apriori knowledge, i.e. a given floor plan, into the parameters of a discrete-state HMM. In contrast to classical position estimation, we use an adapted Viterbi-algorithm for decoding the maximum aposteriori (MAP) trajectory estimate based on the geometry-aware HMM. Results and advantages of the presented method are discussed for the localization of a mobile sensor node in a simulated measurement setup.

We present a new model, derived from classical Hidden Markov Models (HMMs), to learn sequences of large Boolean vectors. Our model — Hidden Markov Model with Patterns, or HMMP — differs from HMM by the fact that it uses patterns to define the emission probability distributions attached to the states. We also present an efficient state merging algorithm to learn this model from training vector sequences. This model and our algorithm are applied to learn Boolean vector sequences used to test integrated circuits. The learned HMMPs are used as test sequence generators. They achieve very high fault coverage, despite their reduced size, which demonstrates the effectiveness of our approach.

A Hidden Markov Model (HMM) is a probabilistic model that has been widely applied to a number of fields since its inception over 30 years ago. Computational Biology, Speech Recognition, and Image Processing are but a few of the application areas of HMMs. We propose an efficient algorithm for learning the parameters of a first order HMM from a state distribution (SD) oracle. The SD oracle provides the learner with the state distribution vector corresponding to a query string in the model. The SD oracle is shown to be necessary for polynomial-time learning in the sense that the consistency problem involving learning HMM parameters from a training set of state distribution vectors without the ability to query the SD oracle, is NP-complete. The learning algorithm proposed is based on an algorithm described by Tzeng for learning Probabilistic Automata.