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.

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.

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.

Integrated top-down and bottom-up proteomics mass spectrometry for the characterization of endogenous ribosomal protein heterogeneity

Ribosomes are abundant,large RNA-protein complexes that are the sites of all protein synthesis in cells.Defects in ribosomal proteins(RPs),including proteoforms arising from genetic variations,alternative splicing of RNA transcripts,post-translational modifications and alterations of protein expression level,have been linked to a diverse range of diseases,including cancer and aging.Comprehensive characterization of ribosomal proteoforms is challenging but important for the discovery of potential disease biomarkers or protein targets.In the present work,using E.coli 70S RPs as an example,we first developed a top-down proteomics approach on a Waters Synapt G2 Si mass spectrometry(MS)system,and then applied it to the HeLa 80S ribosome.The results were complemented by a bottom-up approach.In total,50 out of 55 RPs were identified using the top-down approach.Among these,more than 30 RPs were found to have their N-terminal methionine removed.Additional modifications such as methylation,acetylation,and hydroxylation were also observed,and the modification sites were identified by bottomup MS.In a HeLa 80S ribosomal sample,we identified 98 ribosomal proteoforms,among which multiple truncated 80S ribosomal proteoforms were observed,the type of information which is often overlooked by bottom-up experiments.Although their relevance to diseases is not yet known,the integration of topdown and bottom-up proteomics approaches paves the way for the discovery of proteoform-specific disease biomarkers or targets.

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.

Bottom-up holistic carrier management strategy induced synergistically by multiple chemical bonds to minimize energy losses for efficient and stable perovskite solar cells

The defects from electron transport layer,perovskite layer and their interface would result in carrier nonradiative recombination losses.Poor buried interfacial contact is detrimental to charge extraction and device stability.Here,we report a bottom-up holistic carrier management strategy induced synergistically by multiple chemical bonds to minimize bulk and interfacial energy losses for high-performance perovskite photovoltaics.4-trifluoromethyl-benzamidine hydrochloride(TBHCl)containing–CF_(3),amidine cation and Cl^(-)is in advance incorporated into SnO_(2)colloid solution to realize bottom-up modification.The synergistic effect of multiple functional groups and multiple-bond-induced chemical interaction are revealed theoretically and experimentally.F and Cl^(-)can passivate oxygen vacancy and/or undercoordinated Sn^(4+)defects by coordinating with Sn^(4+).The F can suppress cation migration and modulate crystallization via hydrogen bond with FA^(+),and can passivate lead defects by coordinating with Pb^(2+).The–NH_(2)–C=NH^(+)_(2)and Cl^(-)can passivate cation and anion vacancy defects through ionic bonds with perovskites,respectively.Through TBHCl modification,the suppression of agglomeration of SnO_(2)nanoparticles,bulk and interfacial defect passivation,and release of tensile strains of perovskite films are demonstrated,which resulted in a PCE enhancement from 21.28%to 23.40%and improved stability.With post-treatment,the efficiency is further improved to 23.63%.

Analysis of postoperative ocular surface changes and intervention effect after pars plana vitrectomy in meibomian gland dysfunction dry eye patients

AIM:To observe ocular surface changes after phacovitrectomy in patients with mild to moderate meibomian gland dysfunction(MGD)-type dry eye and track clinical treatment response using a Keratograph 5M and a Lipi View interferometer.METHODS:Forty cases were randomized into control group A and treatment group B;the latter received meibomian gland treatment 3d before phacovitrectomy and sodium hyaluronate before and after surgery.The average non-invasive tear film break-up time(NITBUTav),first noninvasive tear film break-up time(NITBUTf),non-invasive measured tear meniscus height(NTMH),meibomian gland loss(MGL),lipid layer thickness(LLT)and partial blink rate(PBR)were measured preoperatively and 1wk,1 and 3mo postoperatively.RESULTS:The NITBUTav values of group A at 1wk(4.38±0.47),1mo(6.76±0.70),and 3mo(7.25±0.68)were significantly lower than those of group B(7.45±0.78,10.46±0.97,and 11.31±0.89;P=0.002,0.004,and 0.001,respectively).The NTMH values of group B at 1wk(0.20±0.01)and 1mo(0.22±0.01)were markedly higher than those of group A(0.15±0.01 and 0.15±0.01;P=0.008 and P<0.001,respectively);however,there was no difference at 3mo.The LLT of group B at 3mo[91.5(76.25-100.00)]significantly exceeded that of group A[65.00(54.50-91.25),P=0.017].No obvious intergroup difference was found in MGL or PBR(P>0.05).CONCLUSION:Mild to moderate MGD dry eye worsens in the short term after phacovitrectomy.Preoperative cleaning,hot compresses,and meibomian gland massage as well as preoperative and postoperative sodium hyaluronate promote the rapid recovery of tear film stability.

An Efficient Way to Parse Logs Automatically for Multiline Events

In order to obtain information or discover knowledge from system logs,the first step is to performlog parsing,whereby unstructured raw logs can be transformed into a sequence of structured events.Although comprehensive studies on log parsing have been conducted in recent years,most assume that one event object corresponds to a single-line message.However,in a growing number of scenarios,one event object spans multiple lines in the log,for which parsing methods toward single-line events are not applicable.In order to address this problem,this paper proposes an automated log parsing method for multiline events(LPME).LPME finds multiline event objects via iterative scanning,driven by a set of heuristic rules derived from practice.The advantage of LPME is that it proposes a cohesion-based evaluation method for multiline events and a bottom-up search approach that eliminates the process of enumerating all combinations.We analyze the algorithmic complexity of LPME and validate it on four datasets from different backgrounds.Evaluations show that the actual time complexity of LPME parsing for multiline events is close to the constant time,which enables it to handle large-scale sample inputs.On the experimental datasets,the performance of LPME achieves 1.0 for recall,and the precision is generally higher than 0.9,which demonstrates the effectiveness of the proposed LPME.

WT-Parser:一种有效的在线日志解析方法

系统日志是用于记录系统运行情况的重要数据,通过分析日志可以有效监测系统运行状态、检测系统异常。随着大数据,云计算等技术的兴起,系统日志随着系统规模的扩大也不断增加,如何实现日志自动化分析成为当前研究热点,将非结构化日志文本解析为结构化数据是日志分析的基础。针对大多数日志解析方法难以有效分离日志状态变量的问题,文章提出在线日志解析方法(WT-Parser),方法通过日志单词构成来筛选模板单词用于日志聚类,并采用前缀树的思想提取日志模板。实验使用五个真实的日志数据集对方法进行评估,结果表明,WT-Parser能有效区分日志的状态变量,并且在大多数数据集上都显示了更加优异的性能。

ASE-ERNIE:一种基于ERNIE的中文指令动作序列生成方法

中文结构灵活多样,同样的语义有多种表达方式,在中文指令过长或存在多组动作的情况下,家庭服务机器人很难正确理解并做出响应。此外,家庭环境的中文指令解析研究在数据方面仍然稀缺。现有的方法不能准确提取出指令中包含的全部动作序列。由此提出一种基于ERNIE方法的动作序列提取(ASE-ERNIE)模型来提取中文指令中的可执行动作序列,该模型通过动作类型和参数之间的关系提取指令信息,将动作序列的提取工作转化为序列标注任务。在指令中存在多组动作并且存在多个参数的情况下,依然能够提取出全部动作序列。通过实验验证了所提方法的有效性,动作序列提取任务的F 1值达到了80.37%。

融合情感语义与句法结构的中文开放域事理图谱构建研究

为解决大规模开放域事理图谱构建过程中缺少标注数据以及事件类型未知导致的限定域事理图谱构建方法难以迁移的问题,利用规则匹配方法高效识别开放域文本中包含的多种事件逻辑关系,融合情感语义与句法结构信息分析提高事件抽取准确性,以更好完成事理图谱的构建任务。首先,总结并扩展因果、顺承、条件、转折等多种逻辑关系抽取模板,并基于规则模板、依存句法信息筛选逻辑关系事件句;其次,创新性地引入情感语义分析方法,在句法结构信息的基础上,通过捕获事件及事件间关系的情感语义精准识别事件类型,进而抽取事件论元;再次,计算语义相似度,进行事件融合,构建<前序事件,事件逻辑关系,后序事件>三元组,得到事件事理图谱,并进一步进行事件泛化以构建抽象事理图谱;最后,以事件发展较完整的“2022年猴痘事件”为数据源,通过实证分析证明开放域事理图谱构建方法可以实现不同类型事件的识别、事件间逻辑关系的揭露,其有效性、可行性得到验证。研究不仅弥补了现有事理图谱构建理论的不足,也为决策支持、事件发展预测等提供有力的数据支持。