Exploring latent states of problem-solving competence using hidden Markov model on process data

The response process of problem-solving items contains rich information about respondents' behaviours and cognitive process in the digital tasks, while the information extraction is a big challenge. The aim of the study is to use a data-driven approach to explore the latent states and state transitions underlying problem-solving process to reflect test-takers' behavioural patterns, and to investigate how these states and state transitions could be associated with test-takers' performance. We employed the Hidden Markov Modelling approach to identify test takers' hidden states during the problem-solving process and compared the frequency of states and/or state transitions between different performance groups. We conducted comparable studies in two problem-solving items with a focus on the US sample that was collected in PIAAC 2012, and examined the correlation between those frequencies from two items. Latent states and transitions between them underlying the problem-solving process were identified and found significantly different by performance groups. The groups with correct responses in both items were found more engaged in tasks and more often to use efficient tools to solve problems, while the group with incorrect responses was found more likely to use shorter action sequences and exhibit hesitative behaviours. Consistent behavioural patterns were identified across items. This study demonstrates the value of data-driven based HMM approach to better understand respondents' behavioural patterns and cognitive transmissions underneath the observable action sequences in complex problem-solving tasks.     

基于隐马尔可夫模型的股票价格预测组合模型

提出了一种用于股票价格预测的人工神经网络(ANN),隐马尔可夫模型(HMM)和粒子群优化算法(PSO)的组合模型-APHMM模型.在APHMM模型中,ANN算法将股票的每日开盘价、最高价、最低价与收盘价转换为相互独立的量并作为HMM的输入.然后,利用PSO算法对HMM的参数初始值进行优化,并用Baum-Welch算法进行参数训练.经过训练后的HMM在历史数据中找出一组与今天股票的上述4个指标模式最相似数据,加权平均计算每个数据与它后一天的收盘价格差,则今天的股票收盘价加上这个加权平均价格差便为预测的股票收盘价.实验结果表明,APHMM模型具有良好的预测性能.     

基于连续隐马尔可夫模型的人脸识别方法

提出了一种基于连续隐马尔可夫模型的人脸图像识别方法,主要内容包括以下方面:①由于奇异值向量具有稳定性.转置不变性等特点,对归一化的人脸图像,采用奇异值分解抽取人脸图像特征作为观察值序列;②在人脸识别中应用连续隐马尔可夫模型,采用双高斯概率密度函数训练,建立HMM模型,再利用建好的HMM模型进行识别.实验结果显示,所提出的方法减少了数据计算量,运行速度快,并提高了识别率,完全满足人脸识别系统实时性要求.     

基于改进粒子群算法的隐马尔可夫模型训练

针对隐马尔可夫模型传统训练算法易收敛于局部极值的问题,提出一种带极值扰动的自适应调整惯性权重和加速系数的粒子群算法,将改进后的粒子群优化算法引入到隐马尔可夫模型的训练中,分别对隐马尔可夫模型的状态数与参数进优化.通过对手写数字识别的实验说明,提出的基于改进粒子群优化算法的隐马尔可夫模型训练算法与传统隐马尔可夫模型训练算法Baum-Welch算法相比,能有效地跳出局部极值,从而使训练后的隐马尔可夫模型具有较高的识别能力.     

双层隐马尔可夫链的突发流合成

网络流模型被广泛用于构建网络与网络服务的测试环境,其准确性直接影响各种业务的性能评估结果及在实际网络环境中的鲁棒性.随着电子商务及新型网络应用的普及,突发流现象已经成为现代互联网的主要特征之一.针对平稳网络流而设计的传统网络流模型已经难以有效地描述现代网络中突发流的时间结构性及统计属性,从而不能准确反映现代网络流的行为特征.为此,提出一种新的结构化双层隐马尔可夫模型用于模拟实际网络环境下的突发流,并设计了有效的模型参数推断算法及突发流合成方法.该模型通过结构化的2层隐马尔可夫过程描述突发流并实现仿真合成,使合成流可以重现实际突发流的时间结构性、统计特性及自相似性.实验表明,该模型可以有效合成突发流.     

基于隐马尔可夫模型的视频异常场景检测

视频技术的广泛应用带来海量的视频数据,仅依靠人力对监控视频中的异常进行检测是不太可能的。异常行为的自动化检测在公共安全等领域的地位极其重要。提出一种综合考虑目标特性和时空上下文的异常检测方法,该方法利用光流纹理图描述移动物体的刚性特征,建立基于隐马尔可夫模型HMM的时间上下文异常检测模型。在此基础上,提取异常目标的Radon特征,以支持向量机SVM的异常预分类结果为基础,通过HMM建立异常场景的空间上下文分类模型。该模型在公共数据集UCSD PED2上进行了实验验证,结果表明,本算法不仅在异常检测方面优于已有算法,而且还能给出异常分类。     

基于离散HSMM的故障预测模型

提出了一种基于离散HSMM的故障预测模型,根据部分观测矢量预测系统下一时刻处于各个状态的概率。结合HSMM的前向—后向(FB)算法,给出了部分观测下HSMM的状态预测算法。将提出的模型应用于减速箱故障预测中,结果表明该方法可以有效地进行故障预测。     

Detection of heart valve diseases by using fuzzy discrete hidden Markov model

In the present study, biomedical based application was developed to classify the data belongs to normal and abnormal samples generated by Doppler ultrasound. This study consists of raw data obtaining and pre-processing, feature extraction and classification steps. In the pre-processing step, a high-pass filter, white de-noising and normalization were used. During the feature extraction step, wavelet entropy was applied by wavelet transform and short time fourier transform. Obtained features were classified by fuzzy discrete hidden Markov model (FDHMM). For this purpose, a FDHMM that consists of Sugeno and Choquet integrals and λ fuzzy measurement was defined to eliminate statistical dependence assumptions to increase the performance and to have better flexibility. Moreover, Sugeno integral was used together with triangular norms that are mentioned frequently in the literature in order to increase the performance. Experimental results show that recognition rate obtained by Sugeno fuzzy integral with triangular norm is more successful than recognition rates obtained by standard discrete HMM (DHMM) and Choquet integral based FDHMM. In addition to this, it is shown in this study that the performance of the Sugeno integral based method is better than the performances of artificial neural network (ANN) and HMM based classification systems that were used in previous studies of the authors.     

一种基于二维隐马尔可夫模型的图像分类算法

针对图像分块之间的相互依赖关系,提出一种基于二维隐马尔可夫模型的图像分类算 法。该算法将一维隐马尔可夫模型扩展成二维隐马尔可夫模型,模型中相邻的图像分块在平面两个 方向上按条件转移概率进行状态转换,反应出两个维上的依赖关系。隐马尔可夫模型参数通过期望 最大化算法(EM)来估计。同时,本文利用二维Viterbi算法,在训练隐马尔可夫模型的基础上,实现 对图像进行最优分类。文件图像分割的应用表明,隐马尔可夫算法优于CART算法。     

Masquerade detection using profile hidden Markov models

In this paper, we consider the problem of masquerade detection, based on user-issued UNIX commands. We present a novel detection technique based on profile hidden Markov models (PHMMs). For comparison purposes, we implement an existing modeling technique based on hidden Markov models (HMMs). We compare these approaches and show that, in general, our PHMM technique is competitive with HMMs. However, the standard test data set lacks positional information. We conjecture that such positional information would give our PHMM a significant advantage over HMM-based detection. To lend credence to this conjecture, we generate a simulated data set that includes positional information. Based on this simulated data, experimental results show that our PHMM-based approach outperforms other techniques when limited training data is available.     

基于隐马尔可夫模型的电子商务网站结构优化*

针对电子商务网站中部分商品页面不处于用户预期位置,导致用户访问代价较大的情况,提出一种使用隐马尔可夫模型对网站结构进行优化建模的方法。首先建立一个隐马尔可夫模型用于模拟用户带有目的(表现为某种目标商品)的访问过程,再使用该模型挖掘出用户隐藏在访问序列中的目的,最后通过新增超链接实现网站结构优化。实验证明用该方法解决网站结构优化问题具备一定的可行性和有效性。     

基于Contourlet域隐马尔可夫树模型的图像融合

基于Contourlet域的隐马尔可夫树模型能反映不同尺度系数之间、不同方向系数之间的相关性,基于此,提出了一种基于Contourlet域隐马尔可夫树模型的图像融合算法。对源图像进行Contourlet变换,并针对高频子带系数建模并训练得到每一系数的后验概率;利用该后验概率指导高频系数融合的规则,对边缘和背景区域进行不同的融合处理,以尽可能保留原始图像的重要特征;进行Contourlet反变换得到最终融合结果。针对多聚焦图像进行了融合实验,采用联合熵、熵、相关系数、清晰度等指标对融合效果进行评价,实验表明了该算法优于基于Contourlet域的常规融合算法以及小波域隐马尔可夫树融合算法。     

基于隐马尔可夫模型的运动目标轨迹识别 *

引入改进的隐马尔可夫模型算法,针对真实场景中运动目标轨迹的复杂程度对各个轨迹模式类建立相应的隐马尔可夫模型,利用训练样本训练模型得到可靠的模型参数;计算测试样本对于各个模型的最大似然概率,选取最大概率值对应的轨迹模式类作为轨迹识别的结果,对两种场景中聚类后的轨迹进行训练与识别。实验结果表明,平均识别率分别达到87.76 %和94. 19%。     

基于隐马尔可夫模型的内部威胁检测方法

提出了一种基于隐马尔可夫模型的内部威胁检测方法.针对隐马尔可夫模型评估问题的解法在实际应用中存在利用滑动窗口将观测事件序列经过放大处理导致误报率偏高的缺陷,在Windows平台上设计并实现了一个基于系统调用的内部威胁检测原型系统,利用截获Windows Native API的方法,通过程序行为的正常轮廓库来检测程序异常行为模式.实验结果表明,新方法以程序的内在运行状态作为处理对象,正常轮廓库较小,克服了传统评估方法因P(O|λ)值太小而无法有效区分正常与异常的问题,检测性能更好.     

Blind separation of non-stationary sources using continuous density hidden Markov models

Blind source separation (BSS) has attained much attention in signal processing society due to its ‘blind’ property and wide applications. However, there are still some open problems, such as underdetermined BSS, noise BSS. In this paper, we propose a Bayesian approach to improve the separation performance of instantaneous mixtures with non-stationary sources by taking into account the internal organization of the non-stationary sources. Gaussian mixture model (GMM) is used to model the distribution of source signals and the continuous density hidden Markov model (CDHMM) is derived to track the non-stationarity inside the source signals. Source signals can switch between several states such that the separation performance can be significantly improved. An expectation-maximization (EM) algorithm is derived to estimate the mixing coefficients, the CDHMM parameters and the noise covariance. The source signals are recovered via maximum a posteriori (MAP) approach. To ensure the convergence of the proposed algorithm, the proper prior densities, conjugate prior densities, are assigned to estimation coefficients for incorporating the prior information. The initialization scheme for the estimates is also discussed. Systematic simulations are used to illustrate the performance of the proposed algorithm. Simulation results show that the proposed algorithm has more robust separation performance in terms of similarity score in noise environments in comparison with the classical BSS algorithms in determined mixture case. Additionally, since the mixing matrix and the sources are estimated jointly, the proposed EM algorithm also works well in underdetermined case. Furthermore, the proposed algorithm converges quickly with proper initialization.     

A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases

Abstract: Application of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non‐invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy‐C‐means/CHMM (95.12%).     

A robust image steganography using teaching learning based optimization based edge detection model for smart cities

Recently, Internet becomes a most common medium for transferring critical data and the security of the transmitted data gains maximum priority. Image steganography has been developed as a well-known model of data hiding which verifies the security level of the transferred data. The images offer high capacity, and the occurrence of accessibility over the Internet is more. An effective steganography model is required for achieving better embedding capacity and also maintaining the other variables in an acceptable value. This article introduces a new robust image steganography using Teaching Learning Based Optimization (TLBO) edge detection model. The TBLO is basically a metaheuristic algorithm which is inspired from the teaching and learning procedure in classrooms. The former stage indicates the learning from the teacher and the latter phase represents the interaction among the learners. The experimental validation takes place in a comprehensive way under several views and the outcome pointed out the superior results of the presented model.     

Sucker rod pumping diagnosis using valve working position and parameter optimal continuous hidden Markov model

Down-hole operating condition diagnosis based on dynamometer card is a key subject for sucker rod pumping in oil extraction engineering. In this technology, feature extraction and diagnostic model are two indispensable elements. To accurately and automatically diagnose the operating condition by computer, a novel diagnostic method for sucker rod pumping is proposed. The first novel idea is to extract seven geometric features, which are obtained from dynamometer card using barycentric decomposition algorithm and valve working position. The second novel idea focuses on the use of continuous hidden Markov model (CHMM) to create classifiers for diagnosing the down-dole operating conditions and then clonal selection algorithm (CSA) is used to optimize the selection of initial parameters for CHMM. Finally, the proposed method is tested on an oil field dynamometer card set. Furthermore, this technique is compared with some other existing approaches. The simulation results demonstrate that the performance using the method proposed in this paper is satisfactory.     

混合遗传算法和隐马尔可夫模型的Web信息抽取

传统Web信息抽取的隐马尔可夫模型对初值十分敏感和在实际训练中极易得到局部最优模型参数。提出了一种使用遗传算法优化HMM模型参数的Web信息抽取混合算法。该算法使用实数矩阵编码表示染色体,似然概率值为适应度取值,将GA与Baum-Welch算法相结合对HMM模型参数进行全局优化,并且调整GA-HMM的Baum-Welch算法参数实现Web信息抽取。实验结果表明,新的算法在精确度和召回率指标上比传统HMM具有更好的性能。     

基于连续隐Markov 模型的发酵过程软测量方法

针对生物发酵过程中一些生物参量难以用仪表进行在线检测的问题,提出一种基于连续隐Markov模型（CHMM）的发酵过程软测量建模方法.为减少建模过程的计算量,提出了改进最小分类误差准则,用于CHMM软测量模型参数估计.为避免软测量结果在发酵过程监测与控制实际应用中存在的盲目性,提出了在线评价软测量结果可靠性的可信度评价指标.实验结果表明了所提出方法的有效性以及可信度评价指标的实际意义.