Performance Enhancement of XML Parsing Using Regression and Parallelism

The Extensible Markup Language(XML)files,widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications.With the existing Document Object Model(DOM)based parsing,the performance degrades due to sequential processing and large memory requirements,thereby requiring an efficient XML parser to mitigate these issues.In this paper,we propose a Parallel XML Tree Generator(PXTG)algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework(RXPF)that analyzes and predicts performance through profiling,regression,and code generation for efficient parsing.The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel.The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX,SAX,DOM,JDOM,and PXTG on different cores by using multiple file sizes.The regression phase produces the prediction model,based on which the final code for efficient parsing of XML files is produced through the code generation phase.The RXPF framework has shown a significant improvement in performance varying from 9.54%to 32.34%over other existing models used for parsing XML files.

Improved head-driven statistical models for natural language parsing

Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.

Chunk Parsing and Entity Relation Extracting to Chinese Text by Using Conditional Random Fields Model

Currently, large amounts of information exist in Web sites and various digital media. Most of them are in natural lan-guage. They are easy to be browsed, but difficult to be understood by computer. Chunk parsing and entity relation extracting is important work to understanding information semantic in natural language processing. Chunk analysis is a shallow parsing method, and entity relation extraction is used in establishing relationship between entities. Because full syntax parsing is complexity in Chinese text understanding, many researchers is more interesting in chunk analysis and relation extraction. Conditional random fields (CRFs) model is the valid probabilistic model to segment and label sequence data. This paper models chunk and entity relation problems in Chinese text. By transforming them into label solution we can use CRFs to realize the chunk analysis and entities relation extraction.

Design and Implementation of Weibo Sentiment Analysis Based on LDA and Dependency Parsing

Information content security is a branch of cyberspace security. How to effectively manage and use Weibo comment information has become a research focus in the field of information content security. Three main tasks involved are emotion sentence identification and classification,emotion tendency classification,and emotion expression extraction. Combining with the latent Dirichlet allocation(LDA) model,a Gibbs sampling implementation for inference of our algorithm is presented,and can be used to categorize emotion tendency automatically with the computer. In accordance with the lower ratio of recall for emotion expression extraction in Weibo,use dependency parsing,divided into two categories with subject and object,summarized six kinds of dependency models from evaluating objects and emotion words,and proposed that a merge algorithm for evaluating objects can be accurately evaluated by participating in a public bakeoff and in the shared tasks among the best methods in the sub-task of emotion expression extraction,indicating the value of our method as not only innovative but practical.

Two-stage approach to full Chinese parsing

Natural language parsing is a task of great importance and extreme difficulty. In this paper, we present a full Chinese parsing system based on a two-stage approach. Rather than identifying all phrases by a uniform model, we utilize a divide and conquer strategy. We propose an effective and fast method based on Markov model to identify the base phrases. Then we make the first attempt to extend one of the best English parsing models i.e. the head-driven model to recognize Chinese complex phrases. Our two-stage approach is superior to the uniform approach in two aspects. First, it creates synergy between the Markov model and the head-driven model. Second, it reduces the complexity of full Chinese parsing and makes the parsing system space and time efficient. We evaluate our approach in PARSEVAL measures on the open test set, the parsing system performances at 87.53% precision, 87.95% recall.

SUBDIVIDING VERBS TO IMPROVE SYNTACTIC PARSING

This paper proposes a new way to improve the performance of dependency parser: subdividing verbs according to their grammatical functions and integrating the information of verb subclasses into lexicalized parsing model. Firstly,the scheme of verb subdivision is described. Secondly,a maximum entropy model is presented to distinguish verb subclasses. Finally,a statistical parser is developed to evaluate the verb subdivision. Experimental results indicate that the use of verb subclasses has a good influence on parsing performance.

Residual Network with Enhanced Positional Attention and Global Prior for Clothing Parsing

Clothing parsing, also known as clothing image segmentation, is the problem of assigning a clothing category label to each pixel in clothing images. To address the lack of positional and global prior in existing clothing parsing algorithms, this paper proposes an enhanced positional attention module(EPAM) to collect positional information in the vertical direction of each pixel, and an efficient global prior module(GPM) to aggregate contextual information from different sub-regions. The EPAM and GPM based residual network(EG-ResNet) could effectively exploit the intrinsic features of clothing images while capturing information between different scales and sub-regions. Experimental results show that the proposed EG-ResNet achieves promising performance in clothing parsing of the colorful fashion parsing dataset(CFPD)(51.12% of mean Intersection over Union(mIoU) and 92.79% of pixel-wise accuracy(PA)) compared with other state-of-the-art methods.

Video events recognition by improved stochastic parsing based on extended stochastic context-free grammar representation

Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are available to recognize events which happen alternately. The other is that the temporal relationship between atomic actions is not fully utilized. Aiming at these problems, an algo- rithm based on an extended stochastic context-free grammar （SCFG） representation is proposed for events recognition. Events are modeled by a series of atomic actions and represented by an extended SCFG. The extended SCFG can express the hierarchical structure of the events and the temporal re- lationship between the atomic actions. In comparison with previous work, the main contributions of this paper are as follows： ① Events （include alternating events） can be recognized by an improved stochastic parsing and shortest path finding algorithm. ② The algorithm can disambiguate the detec- tion results of atomic actions by event context. Experimental results show that the proposed algo- rithm can recognize events accurately and most atomic action detection errors can be corrected sim- ultaneously.

Fast Chinese syntactic parsing method based on conditional random fields

A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.

A Modular Incremental Model for English Full Parsing

In this paper, we present a modular incremental statistical model for English full parsing. Unlike other full parsing approaches in which the analysis of the sentence is a uniform process, our model separates the full parsing into shallow parsing and sentence skeleton parsing. In shallow parsing, we finish POS tagging, Base NP identification, prepositional phrase attachment and subordinate clause identification. In skeleton parsing, we use a layered feature-oriented statistical method. Modularity possesses the advantage of solving different problems in parsing with corresponding mechanisms. Feature-oriented rule is able to express the complex lingual phenomena at the key point if needed. Evaluated on Penn Treebank corpus, we obtained 89.2% precision and 89.8% recall.

Optimization of Mobile Network Radio Coverage by Automating Radio Parameter Updates Using Parsing

The present work aims is to propose a solution for automating updates (MAJ) of the radio parameters of the ATOLL database from the OSS NetAct using Parsing. Indeed, this solution will be operated by the RAN (Radio Access Network) service of mobile operators, which ensures the planning and optimization of network coverage. The overall objective of this study is to make synchronous physical data of the sites deployed in the field with the ATOLL database which contains all the data of the coverage of the mobile networks of the operators. We have made an application that automates, updates with the following functionalities: import of radio parameters with the parsing method we have defined, visualization of data and its export to the Template of the ATOLL database. The results of the tests and validations of our application developed for a 4G network have made it possible to have a solution that performs updates with a constraint on the size of data to be imported. Our solution is a reliable resource for updating the databases containing the radio parameters of the network at all mobile operators, subject to a limitation in terms of the volume of data to be imported.

Clothing Parsing Based on Multi-Scale Fusion and Improved Self-Attention Mechanism

Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.This paper presents a convolutional structure with multi-scale fusion to optimize the step of clothing feature extraction and a self-attention module to capture long-range association information.The structure enables the self-attention mechanism to directly participate in the process of information exchange through the down-scaling projection operation of the multi-scale framework.In addition,the improved self-attention module introduces the extraction of 2-dimensional relative position information to make up for its lack of ability to extract spatial position features from clothing images.The experimental results based on the colorful fashion parsing dataset(CFPD)show that the proposed network structure achieves 53.68%mean intersection over union(mIoU)and has better performance on the clothing parsing task.

Top-down控制图法评定石墨炉原子吸收光谱法测定血中铅含量的不确定度

采用Top-down控制图法评定石墨炉原子吸收光谱法测定血中铅含量的不确定度。依据GBZ/T 316.1—2018《血中铅的测定第1部分:石墨炉原子吸收光谱法》,不同人员每周测定铅质量浓度为(104±15)μg/L的冻干牛血质控样品1次,连续测定35次。对检测的35组数据进行离群值、正态性、独立性验证,并用Top-down控制图法评定铅含量的不确定度。血中铅质量浓度质控测试结果为(105.97±12.04)μg/L(95%包含概率)。Top-down控制图法评定血中铅含量的不确定度,简单、易操作,可以作为血铅含量检测的质量控制手段,对实验室检测结果进行趋势性分析,发现潜在的影响检测结果的因素,规避实验室风险。

Top-Down技术控制图法评定X射线荧光光谱法测定铁矿中SiO_(2)的不确定度

依据标准GB/6730.62—2005《铁矿石钙、硅、镁、钛、磷、锰、铝和钡含量的测定波长色散X射线荧光光谱法》测定铁矿石中SiO_(2)含量。将SiO_(2)质量分数为8.78%的含砷铁矿标准样品YSBC 14721—98作为质控样品,不同人员不同时间对铁矿质控样品进行测量得到20个数据,离群值检验后,依据国标GB/T 27407—2010和GB/T 27411—2012中评定过程进行Top-Down技术控制图法不确定度评定。期间精密度在重复性和再现性标准偏差之间,评价合理;平均偏倚和“0”没有显著性差异,无需校正。检测某铁矿样品,SiO_(2)质量分数5.24%,由期间精密度带来的不确定度为0.0256%,由偏倚带来的不确定度为0.0313%。由这两项合成的标准不确定度u为0.0404%,取包含概率p=95%,包含因子k=2,扩展不确定度U为0.08%。评定方法既得到了不确定度,又和日常质控结合起来,保证了检测质量,值得实验室推广使用。

Top-down法评定车用柴油中闪点(闭口)的测量不确定度

采用Top-down技术评估车用柴油闭口闪点的不确定度,通过验证测试数据的偏倚和精密度,确认数据受控且样品稳定。计算实验室内期间相对标准偏差(Sp,rel)和重复性相对标准偏差(Sr,rel),并通过外部质量控制评估测定结果偏倚(Sbias,rel)。结果表明,实验室间精密度对不确定度贡献显著,日常检测可通过能力验证数据评估精密度控制。

动载-温度耦合作用下的沥青路面Top-Down裂缝扩展影响因素分析

在严酷低温环境下,动载-温度耦合作用会促进Top-Down裂缝的扩展,降低路面性能。文章基于断裂力学原理,采用数值模拟方法,运用ABAQUS软件构建了有限元模型,研究了在低温环境下,荷载大小、行车速度、面层模量与基层模量对沥青路面Top-Down裂缝扩展特性的影响,并采用灰色关联法进行影响因素敏感性分析。结果表明:荷载增加和行车速度降低会加速裂缝扩展;面层模量和基层模量增加会促进Ⅰ型裂缝的扩展;基层模量与荷载大小是影响裂缝扩展的关键因素。

沥青路面Top-Down裂缝的断裂力学分析

采用移动荷载模式,在有限元方法中利用动态模量主曲线和时间-温度位移因子来表征沥青混合料的力学性质,利用断裂力学的方法分析了温度、车速、裂缝长度和基层类型等对沥青路面Top-Down裂缝裂尖应力强度因子的影响.研究表明随着裂缝长度增加和温度降低,张开型应力强度因子(KⅠ)和剪切型应力强度因子(KⅡ)的峰值增加.行车速度对应力强度因子的作用频率有显著影响,较慢的行车速度会加速Top-Down裂缝的扩展.与采用粒料基层相比,采用半刚性基层可降低裂尖应力强度因子.随着温度升高和裂缝长度增加,KⅠ对裂缝扩展的贡献减小,而KⅡ的贡献逐渐增加.

Top-Down方法在成型模具设计中的应用

介绍模具Top-Down设计的基本思想、设计流程及其主要特点,并基于Pro/Engineer软件平台,以一鞋楦模具为设计对象,例示出模具Top-Down设计方法的主要设计过程。采用先进的Top-Down模具设计方法可以帮助设计者提高设计速度,避免一般设计中容易发生的错误,降低设计制造成本,加快交货期限。

基于PTC Creo的环形防喷器Top-down设计

为实现防喷器参数化和系列化设计,提升设计质量和效率,在PTC Creo软件平台上,运用Top-down方法设计球形胶芯环形防喷器。通过概念设计、骨架模型设计和组装设计,实现设计参数从顶层(布局)向底层(模型)的无缝传递,进而实现设计参数对模型几何的控制。利用PTC Mathcad软件完成防喷器设计计算并与PTC Creo进行数据交换。在此基础上,通过建立参数表实现对35 MPa的某系列球形胶芯环形防喷器系列化设计。设计表明,Top-down方法具有更高的效率,更易于参数的修改,更有利于系列化产品的快速开发。