TONE MODELING BASED ON HIDDEN CONDITIONAL RANDOM FIELDS AND DISCRIMINATIVE MODEL WEIGHT TRAINING

The use of hidden conditional random fields （HCRFs） for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training （DMWT） is proposed. Acoustic and tone scores are scaled by model weights discriminatively trained by the minimum phone error （MPE） criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition （LVCSR） can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.

Adaptive foreground and shadow segmentation using hidden conditional random fields

Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).

Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

PREDICTION OF LOCATION OF HIDDEN ORE DEPOSITS IN THE AGED ORE FIELDS: AN EXAMPLE FROM FENGHUANGSHAN ORE FIELD, TONGLING, CHINA

This paper discusses the strategy for successfully predicting the location of potential hidden ore bodies in aged ore field, and presents the result of location prediction of hidden ore bodies in Fenghuangshan ore field, Tongling. Innovative conceptual targeting procedures based on a genetic understanding of mineralization systems, carefully geological investigation and correct deduction, together with new geochemical and geophysical technology and integrating of comprehensive information are all very important for the successful prediction. In the aged Fenghuangshan ore field, through researching by application of the metallogenic theory of polygenetic compound ore deposits and triple frequency induced polarization method and exploration tectono geochemical method, we predicted location and quality of hidden ore bodies. According to the prediction, hidden high quality Cu Au ore bodies of skarn type and porphyry type have been discovered.

Mathematical Expression Extraction in Text Fields of Documents Based on HMM

Aiming at the problem that the mathematical expressions in unstructured text fields of documents are hard to be extracted automatically, rapidly and effectively, a method based on Hidden Markov Model (HMM) is proposed. Firstly, this method trained the HMM model through employing the symbol combination features of mathematical expressions. Then, some preprocessing works such as removing labels and filtering words were carried out. Finally, the preprocessed text was converted into an observation sequence as the input of the HMM model to determine which is the mathematical expression and extracts it. The experimental results show that the proposed method can effectively extract the mathematical expressions from the text fields of documents, and also has the relatively high accuracy rate and recall rate.

煤田震电协同勘探技术研究与应用

近年来煤田震电协同勘探技术在设计、处理分析方面都取得了较大的发展,从采区最常使用的三维地震勘探、瞬变电磁勘探方法入手,对采集到的各种数据运用主成分分析(PCA)进行融合处理,解译煤矿采区工作面的异常地质体及富水性等隐蔽致灾因素。该技术实现了三维地震、瞬变电磁勘探在数据层面的综合勘探,提高了物探解释煤矿采区工作面地质异常体(富水性)的准确性,为震电协同勘探技术体系的构建提供了新思路,为煤矿安全、高效、绿色生产提供了保障。

烃气组分研究与隐伏矿预测——以箭猪坡矿区为例

烃气具有扩散速度快、运移距离远、携带信息量大等特点,较常规化探方法优势明显。通过五圩矿田箭猪坡矿体与围岩、矿床外围南北边两条化探剖面土壤样品采集与测试分析,研究矿体、围岩烃气组分特征,认为箭猪坡矿床成因与岩浆热液密切相关;对比土壤烃气异常点与不同地质体、土壤烃气背景值的烃气组分标准化曲线特征,筛选与成矿热液密切相关的烃气异常,认为矿床南北两侧的I、II、III号有利勘查区内烃气异常与岩浆热液密切相关,推测形成三排洞、水落、箭猪坡的成矿热液在该区内亦有活动,存在伏矿体,已有钻孔于II号有利区内钻遇厚5cm含闪锌矿包及黄铜矿的石英脉,IV号有利勘查区内烃气异常与变质热液相关,且已发现有汞矿点,推测有隐伏矿体存在。

Malus’ Law Derived from Deterministic Particle Behavior

A polarized beam of energy is usually interpreted as a set of particles, all having the same polarization state. Difference in behavior between the one and the other particle is then explained by a number of counter-intuitive quantum mechanical concepts like probability distribution, superposition, entanglement and quantized spin. Alternatively, I propose that a polarized beam is composed of a set of particles with a cosine distribution of polarization angles within a polarization area. I show that Malus’ law for the intensity of a beam of polarized light can be derived in a straightforward manner from this distribution. I then show that none of the above-mentioned counter-intuitive concepts are necessary to explain particle behavior and that the ontology of particles, passing through a polarizer, can be easily and intuitively understood. I conclude by formulating some questions for follow-up research.

新媒体场域民族虚无主义的叙事转向、隐忧识别及治理进路

当前,民族虚无主义者全面升级了具象化渗透西式价值观的手段,通过媒介语言“潜隐”滥造民族间敏感性议题、以媒介影像“戏谑”剔除民族严肃性记忆、以媒介技术碎片化“迷惑”和肢解民族自豪感,造成受众的民族认同模糊、精神信仰瓦解和公共理性偏离等三大隐忧。鉴于此,一是要引导媒介受众群体树立民族自信,以大历史观正确看待中华文明的光辉历程;二是要端正全民媒介素养,提升在信息茧房中的“破茧之能”;三是要以习近平文化思想为指引,净化新媒体场域的空间生态;四是要破除颠覆性新传媒技术的垄断壁垒,筑起新媒体场域意识形态的技术高墙。这对于中华民族在新媒体时代汇聚精神伟力、凝聚民族合力具有重大意义。

基于隐条件随机场的自适应视频分割算法

视频目标分割是视频监视与视频目标跟踪、视频目标识别以及视频编辑的基础.本文提出了一种基于隐条件随机场(Hidden conditional random fields,HCRF)的自适应视频分割算法,利用HCRF模型对视频序列中的时空邻域关系建模.使用在线学习的方式对相应的参数进行调整,实现对时空邻域约束关系的权重调整,提高视频目标分割细节上的效果.大量的数据测试表明,与高斯混合模型(Gaussian mixture model,GMM)和联合时空的马尔可夫随机场(Markov random fields,MRF)等算法相比,该算法的分割错误率分别降低了23%和19%.

基于ECC和HFE的纠错密码构造

纠错密码是一种利用纠错码体制来实现纠错和加密双重功能的一种密码体制。大部分已知的纠错密码从变换的角度看是一种对明文的线性变换。从密码分析的角度看,由于不具有非线性变换,密码的混淆能力不强,容易被攻击。利用纠错码(Error-Correction Code,ECC)改造基本HFE(Hidden Field Equations)密码算法,所得的新密码算法具有纠错和加密功能,而且因其具有概率密码特性以及建立在MQ困难问题之上,具有很高的安全强度。

基于轮廓特征的人体行为识别

提出了一种以人的动作序列图像的轮廓为特征、基于隐条件随机场的行为识别方法。首先,利用背景差分法和阴影消除技术提取运动人体轮廓。星型骨架方法只采用单一质心-边界距离来描述人体轮廓,因此对轮廓的局部特征描述能力较弱。定义一种新的基于距离组的轮廓描述方法,它将时变的2D轮廓形状转换为对应的1D距离向量。最后利用判别隐条件随机场对行为进行训练和识别。实验结果表明,本方法的正确识别率达到91.4%以上,识别结果较为理想。

结合HMRF模型的模糊ISODATA高分辨率遥感图像分割

模糊ISODATA(Fuzzy ISODATA,FISODATA)在应用于图像分割时,FISODATA算法定义的FCM目标函数未考虑邻域像素间的数据相关性,导致该算法的抗噪性能较差;此外,FISODATA算法中分裂-合并操作需人工选取阈值参数,而不适当的阈值往往使得该算法陷入局部极值,因而得到错误的类属数并影响图像分割结果。该文将考虑邻域关系的基于隐马尔可夫随机场(Hidden Markov Random Field,HMRF)FCM(HMRF-FCM)方法纳入ISODATA框架,提出HMRF-FCM ISODATA(HMRF-FISODATA)算法,在分裂与合并操作后增加了优化操作,并根据优化结果自适应调节控制聚类分裂与合并的各阈值。该算法不仅能够快速获取正确类属数,而且克服了FISODATA算法没有考虑邻域像素的关系、人工选取阈值参数和受图像噪声影响大等问题,实现了自动正确确定类属数的同时完成高精度图像分割。

基于贪心EM算法的HMRF遥感影像变化检测

提出一种基于贪心EM算法的HMRF遥感影像变化检测算法。该算法采取PCA与差值法相结合的方式来构造差分影像。首先,采用隐马尔可夫随机场(Hidden Markov Random Field,HMRF)模型描述空间上下文信息,并构造系统能量函数;然后,利用贪心EM算法克服EM算法假定混合成分数为已知、迭代结果过分依赖初始值、可能收敛到局部最大点或收敛到参数空间边界的缺点,能够准确学习分布模型结构和参数,发现数据对模型的最佳匹配;最后,通过条件迭代模型(Iterated Conditional Modes,ICM)优化算法求解能量函数最优解,获取变化区域。实验结果表明,该算法能够更好地保持影像的结构性,有效去除孤立噪声。

基于隐马尔科夫度量场模型的车辆检测和跟踪

针对多目标检测和跟踪过程中常发生的合并和分裂现象,提出一种对不同运动目标假设不同的运动模型,并基于隐马尔科夫度量场(HMMF)的检测和跟踪算法.为了更准确地估计目标的运动参数,还提出了一种简单有效的单目相机标定算法.仿真结果表明,文中算法对遮挡不敏感,即使在发生遮挡、合并或分裂情况时也能很好地跟踪运动目标.

隐性课程视阈下“连带学习”、“软技能”与“场”效应问题研究

笔者从隐性课程角度入手,试图厘清三者在基本概念的内涵和外延方面的异同,探讨在隐性课程视阈下"连带学习"、"软技能"与"场"效应三者之间的关联性及作用原理,使得研究者在高等教育的隐性课程理论研究方面进一步拓展研究视野。辨析三者之间的相互关系,使得高校隐性课程实现受教育者的教育价值和高校课程目的的近似统一。

基于条件随机域的生物命名实体识别

提出一种基于条件随机域模型的生物命名实体识别方法,结合单词构词特性以及距离依赖特性,在JNLPBA的GENIAV3.02数据上进行实验,测试结果表明,引入距离依赖后,系统的识别性能比只利用单特性的条件随机域方法提高2.54%,可获得较好的识别效果,提高了系统的识别效率。

结合高斯回归模型和隐马尔可夫随机场的模糊聚类图像分割

为了克服经典模糊聚类图像分割算法对图像噪声的敏感性,该文提出结合高斯回归模型(GRM)和隐马尔科夫随机场(HMRF)的模糊聚类图像分割算法。该算法用信息熵正则化模糊C均值(FCM)的目标函数,再用KL(Kullback-Leibler)信息加以改进,并将HMRF和GRM模型应用到该目标函数中,其中HMRF模型通过先验概率建立标号场邻域关系,而GRM模型则在中心像素标号与其邻域像素标号一致的基础上建立特征场邻域关系。利用提出的算法和其它经典算法分别对模拟图像、真实SAR图像以及纹理图像进行了分割实验,并对分割结果进行精度评价。实验结果表明,该文提出的算法具有更高的分割精度。

融合HCRF和AAM的足球视频精彩事件检测

精彩事件检测在体育视频语义分析领域具有很高的学术研究价值和广泛的市场应用前景.利用隐条件随机场(hidden conditional random field,HCRF)模型在表达和识别语义事件方面的强大功能,创新性地提出了一种融合了HCRF和情感激励模型(affective arousal model,AAM)的精彩事件检测方法.首先,通过精彩事件视频结构语义分析,定义了13种多模态语义线索,以准确描述精彩事件富含的语义信息;其次,在基于概念格的多模态语义线索聚类基础上,添加时域特征信息,以构建特征值加权的情感激励模型,得到了各类精彩事件的情感激励值;最后,在小规模训练样本情况下,有效建立了各类精彩事件检测的HCRF模型,基于视频语义镜头序列、情感激励值序列和精彩事件之间的映射关系,从多模态语义线索、视频结构语义、情感语义等多个维度挖掘了精彩事件的潜在规律,实现了同一HCRF模型下各类精彩事件的同时检测.实验证明了该方法的有效性.

基于TM图像的农业区域植被覆盖变化检测

以交叉相关光谱匹配(cross correlogram spectral matching,CCSM)为基础构建土地覆盖变化强度指标,利用华北农业植被覆盖区2期不同时相的TM图像计算该地区土地覆盖变化强度图像。认为变化强度图像任意二阶邻域中像素的变化强度服从隐马尔可夫模型,用马尔可夫随机场-最大后验估计(maxium a posteriori estimation of markovrandom field,MRF-MAP)的方法从变化强度图像中提取植被变化区域。实验证明:该方法能够有效识别各种外源噪声造成的农业植被覆盖区域同物异谱的现象,可准确提取植被变化区域;但对于水体区域存在误判现象。