Hidden Markov models for gene sequence classification

The article presents an application of hidden Markov models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the variant surface glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host’s immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.     

Conditional Random Fields for Integrating Local Discriminative Classifiers

Conditional random fields (CRFs) are a statistical framework that has recently gained in popularity in both the automatic speech recognition (ASR) and natural language processing communities because of the different nature of assumptions that are made in predicting sequences of labels compared to the more traditional hidden Markov model (HMM). In the ASR community, CRFs have been employed in a method similar to that of HMMs, using the sufficient statistics of input data to compute the probability of label sequences given acoustic input. In this paper, we explore the application of CRFs to combine local posterior estimates provided by multilayer perceptrons (MLPs) corresponding to the frame-level prediction of phone classes and phonological attribute classes. We compare phonetic recognition using CRFs to an HMM system trained on the same input features and show that the monophone label CRF is able to achieve superior performance to a monophone-based HMM and performance comparable to a 16 Gaussian mixture triphone-based HMM; in both of these cases, the CRF obtains these results with far fewer free parameters. The CRF is also able to better combine these posterior estimators, achieving a substantial increase in performance over an HMM-based triphone system by mixing the two highly correlated sets of phone class and phonetic attribute class posteriors.     

基于HMM的维吾尔语连续语音识别系统

维吾尔语是黏着性语言,利用丰富的词缀可以用同样的词干产生超大词汇,给维吾尔语语音识别的研究工作带来了很大困难。结合维吾尔语自身特点,建立了维吾尔语连续语音语料库,利用HTK（HMMToolKit）工具实现了基于隐马尔可夫模型（HMM）的维吾尔语连续语音识别系统。在声学层,选取三音子作为基本的识别单元,建立了维吾尔语的三音子声学模型,并使用决策树、三音子绑定、修补哑音、增加高斯混合分量等方法提高模型的识别精度。在语言层,使用了适合于维吾尔语语音特征的基于统计的二元文法语言模型。最后,利用该系统进行了维吾尔语连续语音识别实验。     

Automatic determination of acoustic model topology using variational Bayesian estimation and clustering for large vocabulary continuous speech recognition

We describe the automatic determination of a large and complicated acoustic model for speech recognition by using variational Bayesian estimation and clustering (VBEC) for speech recognition. We propose an efficient method for decision tree clustering based on a Gaussian mixture model (GMM) and an efficient model search algorithm for finding an appropriate acoustic model topology within the VBEC framework. GMM-based decision tree clustering for triphone HMM states features a novel approach designed to reduce the overly large number of computations to a practical level by utilizing the statistics of monophone hidden Markov model states. The model search algorithm also reduces the search space by utilizing the characteristics of the acoustic model. The experimental results confirmed that VBEC automatically and rapidly yielded an optimum model topology with the highest performance.     

Some properties of the disjunctive languages contained in <Emphasis Type="Italic">Q</Emphasis>

The set of all primitive words Q over an alphabet X was first defined and studied by Shyr and Thierrin (Proceedings of the 1977 Inter. FCT-Conference, Poznan, Poland, Lecture Notes in Computer Science 56. pp. 171–176 (1977)). It showed that for the case |X| ≥ 2, the set along with \({Q^{(i)} = \{f^i\,|\,f \in Q\}, i\geq 2}\) are all disjunctive. Since then these disjunctive sets are often be quoted. Following Shyr and Thierrin showed that the half sets \({Q_{ev} = \{f \in Q\,|\,|f| = {\rm even}\}}\) and Q _od = Q \ Q _ev of Q are disjunctive, Chien proved that each of the set \({Q_{p,r}= \{u\in Q\,|\,|u|\equiv r\,(mod\,p) \},\,0\leq r < p}\) is disjunctive, where p is a prime number. In this paper, we generalize this property to that all the languages \({Q_{n,r}= \{u\in Q\,|\,|u|\equiv r\,(mod\,n) \},\, 0\leq r < n}\) are disjunctive languages, where n is any positive integer. We proved that for any n ≥ 1, k ≥ 2, (Q _n,0) ^k are all regular languages. Some algebraic properties related to the family of languages {Q _n,r | n ≥ 2, 0 ≤ r < n } are investigated.     

基于DDBHMM的维吾尔语音声学识别

在维吾尔语连续语音识别试验的声学层建模基础上,引用DDBHMM模型将上下文相关的三音子作为基本识别单元,并提出一种状态绑定的思想,对状态进行优化。为得到更充分的训练模型,提高识别效率,对语料库进行扩充,在多组对比试验的基础上,分析扩充前后对声学层识别速度、准确率等各个方面的影响。     

3D text segmentation and recognition using leap motion

In this paper, we present a method of Human-Computer-Interaction (HCI) through 3D air-writing. Our proposed method includes a natural way of interaction without pen and paper. The online texts are drawn on air by 3D gestures using fingertip within the field of view of a Leap motion sensor. The texts consist of single stroke only. Hence gaps between adjacent words are usually absent. This makes the system different as compared to the conventional 2D writing using pen and paper. We have collected a dataset that comprises with 320 Latin sentences. We have used a heuristic to segment 3D words from sentences. Subsequently, we present a methodology to segment continuous 3D strokes into lines of texts by finding large gaps between the end and start of the lines. This is followed by segmentation of the text lines into words. In the next phase, a Hidden Markov Model (HMM) based classifier is used to recognize 3D sequences of segmented words. We have used dynamic as well as simple features for classification. We have recorded an overall accuracy of 80.3 % in word segmentation. Recognition accuracies of 92.73 % and 90.24 % have been recorded when tested with dynamic and simple features, respectively. The results show that the Leap motion device can be a low-cost but useful solution for inputting text naturally as compared to conventional systems. In future, this may be extended such that the system can successfully work on cluttered gestures.     

藏语拉萨话大词表连续语音识别声学模型研究

根据藏语的特点,提出藏语拉萨话大词表连续语音识别声学模型,利用高层次的藏语语言知识减少模式匹配的模糊性。以音素和声韵母为声学建模单元,在HTK平台上建立上下文相关的连续隐马尔可夫声学模型,以实现藏语拉萨话特定人大词表连续语音识别。实验结果表明,在最优情况下,该模型词错误率只有7.8%。     

A Dynamic Gesture and Posture Recognition System

This paper presents a real time dynamic hand gesture and posture recognition system based on a neural network and a Hidden Markov Model. For skin color segmentation an adaptive online trained skin color model is used, while the hand posture recognition is accomplished through a likelihood-based classification technique of geometric features. A novel trajectory smoothing technique based on Self Organized Neural Network is introduced to improve HMM classification performance of dynamic gestures. The aim of the proposed system is the creation of a visual dictionary combining hand postures and dynamic gestures. The system has been successfully tested with many people under varying light conditions and different web cameras.     

SAGE: Semantic Annotation of Georeferenced Environments

The emergence of portable 3D mapping systems are revolutionizing the way we generate digital 3D models of environments. These systems are human-centric and require the user to hold or carry the device while continuously walking and mapping an environment. In this paper, we adapt this unique coexistence of man and machines to propose SAGE (Semantic Annotation of Georeferenced Environments). SAGE consists of a portable 3D mobile mapping system and a smartphone that enables the user to assign semantic content to georeferenced 3D point clouds while scanning a scene. The proposed system contains several components including touchless speech acquisition, background noise adaptation, real time audio and vibrotactile feedback, automatic speech recognition, distributed clock synchronization, 3D annotation localization, user interaction, and interactive visualization. The most crucial advantage of SAGE technology is that it can be used to infer dynamic activities within an environment. Such activities are difficult to be identified with existing post-processing semantic annotation techniques. The capability of SAGE leads to many promising applications such as intelligent scene classification, place recognition and navigational aid tasks. We conduct several experiments to demonstrate the effectiveness of the proposed system.     

A formal algorithmic language FALGOL. Thirty years after

FALGOL (Formal ALGOrithmic Language) is a fundamental theoretical model of high-level operational languages with unrestricted program object hierarchy. This model formalizes binding, assignment, substitution, and recursion; moreover, the principle of dynamic binding is implemented in the model in contrast to other formal systems of this sort, which makes FALGOL appropriate to specify the most difficultly formalized concepts in modern object programming languages.     

基于三音素动态贝叶斯网络模型的大词汇量连续语音识别

考虑连续语音中的协同发音现象,基于词-音素结构的DBN(WP-DBN)模型和词-音素-状态结构的DBN(WPS-DBN)模型,引入上下文相关的三音素单元,提出两个新颖的单流DBN模型:基于词-三音素结构的DBN(WT-DBN)模型和基于词-三音素-状态的DBN(WTS-DBN)模型.WTS-DBN模型是三音素模型,识别基元为三音素,以显式的方式模拟了基于三音素状态捆绑的隐马尔可夫模型(HMM).大词汇量语音识别实验结果表明:在纯净语音环境下,WTS-DBN模型的识别率比HMM,WT-DBN,WP-DBN和WPS-DBN模型的识别率分别提高了20.53%,40.77%,42.72%和7.52%.     

Transit cosmological models with domain walls in <Emphasis Type="Italic">f</Emphasis>(<Emphasis Type="Italic">R,T</Emphasis>) gravity

We study the physical behavior of the transition of a 5D perfect fluid universe from an early decelerating phase to the current accelerating phase in the framework of f(R, T) theory of gravity in the presence of domain walls. The fifth dimension is not observed because it is compact. To determine the solution of the field equations, we use the concept of a time-dependent deceleration parameter which yields the scale factor a(t) = sinh^1/n(αt), where n and α are positive constants. For 0 < n ≤ 1, this generates a class of accelerating models, while for n > 1 the universe attains a phase transition from an early decelerating phase to the present accelerating phase, consistent with the recent observations. Some physical and geometric properties of the models are also discussed.     

A stochastic version of Expectation Maximization algorithm for better estimation of Hidden Markov Model

This paper attempts to overcome the local convergence problem of the Expectation Maximization (EM) based training of the Hidden Markov Model (HMM) in speech recognition. We propose a hybrid algorithm, Simulated Annealing Stochastic version of EM (SASEM), combining Simulated Annealing with EM that reformulates the HMM estimation process using a stochastic step between the EM steps and the SA. The stochastic processes of SASEM inside EM can prevent EM from converging to a local maximum and find improved estimation for HMM using the global convergence properties of SA. Experiments on the TIMIT speech corpus show that SASEM obtains higher recognition accuracies than the EM.     

Clustering for malware classification

In this research, we apply clustering techniques to the malware classification problem. We compute clusters using the well-known K-means and Expectation Maximization algorithms, with the underlying scores based on Hidden Markov Models. We compare the results obtained from these two clustering approaches and we carefully consider the interplay between the dimension (i.e., number of models used for clustering), and the number of clusters, with respect to the accuracy of the clustering.     

(1+4)-dimensional spherically symmetric black holes in <Emphasis Type="Italic">f</Emphasis>(<Emphasis Type="Italic">T</Emphasis>)

New non-vacuum spherically symmetric solutions in (1+4)-dimensional space-time are derived using the field equations of f(T) theory, where T is the torsion scalar defined as \(T\mathop = \limits^{def} {T^\mu }_{\nu \rho }S_\mu ^{\nu \rho }\). The energy density, radial and transversal pressures in these solutions are shown to satisfy the energy conditions. Other interesting solutions are obtained under the constraint of vanishing radial pressure for different choices of f(T). Impositions are provided to reproduce the (1+4)-dimensional AdS-Schwarzschild solution. In the quadratic case, i.e., f(T) ∝ T ², other impositions are derived and have shown to satisfy the non-diagonal components of the field equations of f(T) theory. The physics relevant to the resulting models is discussed.     

Combining emerging patterns with random forest for complex activity recognition in smart homes

New healthcare technologies are emerging with the increasing age of the society, where the development of smart homes for monitoring the elders’ activities is in the center of them. Identifying the resident’s activities in an apartment is an important module in such systems. Dense sensing approach aims to embed sensors in the environment to report the detected events continuously. The events are segmented and analyzed via classifiers to identify the corresponding activity. Although several methods were introduced in recent years for detecting simple activities, the recognition of complex ones requires more effort. Due to the different time duration and event density of each activity, finding the best size of the segments is one of the challenges in detecting the activity. Also, using appropriate classifiers that are capable of detecting simple and interleaved activities is the other issue. In this paper, we devised a two-phase approach called CARER (Complex Activity Recognition using Emerging patterns and Random forest). In the first phase, the emerging patterns are mined, and various features of the activities are extracted to build a model using the Random Forest technique. In the second phase, the sequences of events are segmented dynamically by considering their recency and sensor correlation. Then, the segments are analyzed by the generated model from the previous phase to recognize both simple and complex activities. We examined the performance of the devised approach using the CASAS dataset. To do this, first we investigated several classifiers. The outcome showed that the combination of emerging patterns and the random forest provide a higher degree of accuracy. Then, we compared CARER with the static window approach, which used Hidden Markov Model. To have a fair comparison, we replaced the dynamic segmentation module of CARER with the static one. The results showed more than 12% improvement in f-measure. Finally, we compared our work with Dynamic sensor segmentation for real-time activity recognition, which used dynamic segmentation. The f-measure metric demonstrated up to 12.73% improvement.     

A closed asynchronous dynamic model of cellular learning automata and its application to peer-to-peer networks

Cellular Learning Automata (CLAs) are hybrid models obtained from combination of Cellular Automata (CAs) and Learning Automata (LAs). These models can be either open or closed. In closed CLAs, the states of neighboring cells of each cell called local environment affect on the action selection process of the LA of that cell whereas in open CLAs, each cell, in addition to its local environment has an exclusive environment which is observed by the cell only and the global environment which can be observed by all the cells in CLA. In dynamic models of CLAs, one of their aspects such as structure, local rule or neighborhood radius may change during the evolution of the CLA. CLAs can also be classified as synchronous CLAs or asynchronous CLAs. In a synchronous CLA, all LAs in different cells are activated synchronously whereas in an asynchronous CLA, the LAs in different cells are activated asynchronously. In this paper, a new closed asynchronous dynamic model of CLA whose structure and the number of LAs in each cell may vary with time has been introduced. To show the potential of the proposed model, a landmark clustering algorithm for solving topology mismatch problem in unstructured peer-to-peer networks has been proposed. To evaluate the proposed algorithm, computer simulations have been conducted and then the results are compared with the results obtained for two existing algorithms for solving topology mismatch problem. It has been shown that the proposed algorithm is superior to the existing algorithms with respect to communication delay and average round-trip time between peers within clusters.     

Novel steganographic method based on generalized <Emphasis Type="Italic">K</Emphasis>-distance <Emphasis Type="Italic">N</Emphasis>-dimensional pixel matching

In this paper, a steganographic scheme adopting the concept of the generalized K _d -distance N-dimensional pixel matching is proposed. The generalized pixel matching embeds a B-ary digit (B is a function of K and N) into a cover vector of length N, where the order-d Minkowski distance-measured embedding distortion is no larger than K. In contrast to other pixel matching-based schemes, a N-dimensional reference table is used. By choosing d, K, and N adaptively, an embedding strategy which is suitable for arbitrary relative capacity can be developed. Additionally, an optimization algorithm, namely successive iteration algorithm (SIA), is proposed to optimize the codeword assignment in the reference table. Benefited from the high dimensional embedding and the optimization algorithm, nearly maximal embedding efficiency is achieved. Compared with other content-free steganographic schemes, the proposed scheme provides better image quality and statistical security. Moreover, the proposed scheme performs comparable to state-of-the-art content-based approaches after combining with image models.