A New Part-of-Speech Tagging System Based on Closed-words,Word Form and Rules

A method of part-of-speech tagging of English text based on closed-words, wold-form and rules, its abstract model and formal description of its realizing procedure are presented. Finally, an experimental example is givento illustrate the application of this method.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

BPR-UserRec:a personalized user recommendation method in social tagging systems

Social tagging is one of the most important characteristics of Web 2.0 services, and social tagging systems （STS） are becoming more and more popular for users to annotate, organize and share items on the Web. Moreover, online social network has been incorporated into social tagging systems. As more and more users tend to interact with real friends on the Web, personalized user recommendation service provided in social tagging systems is very appealing. In this paper, we propose a personalized user recommendation method, and our method handles not only the users＇ interest networks, but also the social network information. We empirically show that our method outperforms a state-of-the-art method on real dataset from Last.fro dataset and Douban.

Molecular analysis of rice plants harboring a multi-functional T-DNA tagging system

About 25,000 rice T-DNA insertional mutant lines were generated using the vector pCAS04 which has both promoter-trapping and activation-tagging function. Southern blot analysis revealed that about 40% of these mutants were single copy integration and the average T-DNA insertion number was 2.28. By extensive phenotyping in the field, quite a number of agronomically important mutants were obtained. Histochemical GUS assay with 4,310 primary mutants revealed that the GUS-staining frequency was higher than that of the previous reports in various tissues and especially high in flowers. The T-DNA flanking sequences of some mutants were isolated and the T-DNA insertion sites were mapped to the rice genome. The flanking sequence analysis demonstrated the different integration pattern of the right border and left border into rice genome. Compared with Arabidopsis and poplar, it is much varied in the T-DNA border junctions in rice.

Tax-folks hybrid navigation:A new model for resource aggregation in social tagging system

With emergence of Web 2.0,taxonomy and folksonomy have shown a trend of complementarity and integration,and a hybrid tax-folks navigation structure has become a new way to facilitate resource aggregation in social tagging system.In this paper,the generation mechanism of tax-folks hybrid navigation model is analyzed,and the tax-folks hybrid navigation model,which consist of six modules:data preparation,concept lattice construction,concept lattice analysis,tax-folks mapping,tax-folks hybrid navigation tree and outputs& evaluation,is constructed with the theory of formal concept analysis.The study finds that tax-folks hybrid navigation model takes into account the advantages of taxonomy and folksonomy,achieves a tree-like resource aggregation,and effectively improves the resources-finding ability in social tagging system.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to collisions.Based on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is proposed.The algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame observation.By grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID systems.Simulation outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

Molecular Tagging of a New Resistance Gene to Maize Dwarf Mosaic Virus Using Microsatellite Markers

With joint analysis based on the parents, F 1, F 2 and backcrosses, the authors found that the resistance of the maize inbred line Huangzaosi to the maize dwarf mosaic virus strain B was conditioned by a major gene and polygene, and identified a new major gene. Bulked segregate and microsatellite analysis of a F 2 progeny from the combination of Huangzaosi×Mo17 were used to identify the resistance gene, mdm1(t), on the long arm of chromosome 6. This new resistance gene is tightly linked to and located between the microsatellite markers loci, phi077 and bnlg391. The linkage distances between phi077-mdm1(t) and mdm1(t)-bnlg391 are 4.74 centiMorgan (cM) and 6.72 cM respectively.

Connotea中Social Tagging机制研究

选取Connotea为研究对象,统计分析标签、被标引资源、标引者两两关联关系,发现标签覆盖资源范围较广,标签共现现象突出,部分用户标引活跃,资源平均标签数较低,用户对内容关注度规律性转移,科研领域的用户比较多地使用规范词。提出增加标引词结构关联和细粒内容定位标引。

国外Social Tagging研究群体分析

基于抽取的国外知名数据库的学术文献,分析探讨Social Tagging研究群体的学科特征和研究视角。结果表明当前Social Tagging的研究群体主要局限于信息科学与计算机科学,且未形成居于统治权威地位的研究团体和研究人员。自从Social Tagging产生以来,"Social Tagging的组成功能"的研究一直是Social Tagging的基础性研究维度,不过随着应用深入发展,研究维度逐步转而融合信息检索和人机交互这两个相关领域知识分析"Tagger的行为及Social Tagging的界面设计"和"Social Tagging自身的组织和使用"这两个主要研究维度。

基于OPC Systems.NET智能建筑系统集成初探

论文以智能建筑集成的系统研发为应用背景,介绍OPC Systems.NET特性和智能客户端部署,分析与系统集成关联的技术,并深入探讨基于OPC Systems.NET平台的集成方法。

Molecular tagging techniques and their applications to the study of complex thermal flow phenomena

This review article reports the recent progress in the development of a new group of molecule-based flow diagnostic techniques, which include molecular tag- ging velocimetry （MTV） and molecular tagging thermometry （MTT）, for both qualitative flow visualization of thermally induced flow structures and quantitative whole-field mea- surements of flow velocity and temperature distributions. The MTV and MTT techniques can also be easily combined to result in a so-called molecular tagging velocimetry and ther- mometry （MTV＆T） technique, which is capble of achieving simultaneous measurements of flow velocity and temperature distribution in fluid flows. Instead of using tiny particles, the molecular tagging techniques （MTV, MTT, and MTV＆T） use phosphorescent molecules, which can be turned into long-lasting glowing marks upon excitation by photons of appropriate wavelength, as the tracers for the flow veloc- ity and temperature measurements. The unique attraction and implementation of the molecular tagging techniques are demonstrated by three application examples, which include： （1） to quantify the unsteady heat transfer process from a heated cylinder to the surrounding fluid flow in order to exam- ine the thermal effects on the wake instabilities behind the heated cylinder operating in mixed and forced heat convec- tion regimes, （2） to reveal the time evolution of unsteady heat transfer and phase changing process inside micro-sized, icing water droplets in order to elucidate the underlying physics pertinent to aircraft icing phenomena, and （3） to achievesimultaneous droplet size, velocity and temperature measure- ments of ＂in-flight＂ droplets to characterize the dynamic and thermodynamic behaviors of flying droplets in spray flows.

Unified Framework of Performing Chinese Word Segmentation and Part-of-Speech Tagging

The paper proposes a unified framework to combine the advantages of the fast one-at-a-time approach and the high-performance all-at-once approach to perform Chinese Word Segmentation(CWS) and Part-of-Speech(PoS) tagging.In this framework,the input of the PoS tagger is a candidate set of several CWS results provided by the CWS model.The widely used one-at-a-time approach and all-at-once approach are two extreme cases of the proposed candidate-based approaches.Experiments on Penn Chinese Treebank 5 and Tsinghua Chinese Treebank show that the generalized candidate-based approach outperforms one-at-a-time approach and even the all-at-once approach.The candidate-based approach is also faster than the time-consuming all-at-once approach.The authors compare three different methods based on sentence,words and character-intervals to generate the candidate set.It turns out that the word-based method has the best performance.

Improved hidden Markov model for speech recognition and POS tagging

In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.

Global myocardial strain assessment by different imaging modalities to predict outcomes after ST-elevation myocardial infarction:A systematic review

AIM: To conduct a systematic review relating myocardial strain assessed by different imaging modalities for prognostication following ST-elevation myocardial infarction(STEMI).METHODS: An online literature search was performed in Pub Med and OVID&#174; electronic databases to identify any studies that assessed global myocardial strain parameters using speckle-tracking echocardiography(STE) and/or cardiac magnetic resonance imaging(CMR) techniques [either myocardial tagging or feature tracking(FT) software] in an acute STEMI cohort(days 0-14 post-event) to predict prognosis [either development of major adverse cardiac events(MACE)] or adverse left ventricular(LV) remodelling at follow-up(≥ 6 mo for MACE,≥ 3 mo for remodelling). Search was restricted to studies within the last 20 years. All studies that matched the pre-defined search criteria were reviewed and their results interpreted. Due to considerable heterogeneity between studies,metaanalysis was not performed.RESULTS: A total of seven studies(n = 7) were identified that matched the search criteria. All studies used STE to evaluate strain parameters- five(n = 5) assessed global longitudinal strain(GLS)(n = 5),one assessed GLS rate(GLS-R)(n = 1) and one assessed both(n = 1). Three studies showed that GLS independently predicted the development of adverse LV remodelling by multivariate analysis- odds ratio between 1.19(CI: 1.04-1.37,P < 0.05) and 10(CI: 6.7-14,P < 0.001) depending on the study. Four studies showed that GLS predicted the development of MACE- hazard ratio(HR) between 1.1(CI: 1-1.1,P = 0.006) and 2.34(1.10-4.97,P < 0.05). One paper found that GLS-R could significantly predict MACEHR 18(10-35,P < 0.001)- whilst another showed it did not. GLS <-10.85% had sensitivity/specificity of 89.7%/91% respectively for predicting the development of remodelling whilst GLS <-13% could predict the development of MACE with sensitivity/specificity of 100%/89% respectively. No suitable studies were identified that assessed global strain by CMR tagging or FT techniques.CONCLUSION: GLS measured acutely post-STEMI by STE is a predictor of poor prognosis. Further research is needed to show that this is true for CMR-based techniques.

A Cross Layer Protocol for Fast Identification of Blocked Tags in Large-Scale RFID Systems

Blocker tag attack is one of the denial-of-service(DoS)attacks that threatens the privacy and security of RFID systems.The attacker interferes with the blocked tag by simulating a fake tag with the same ID,thus causing a collision of message replies.In many practical scenarios,the number of blocked tags may vary,or even be small.For example,the attacker may only block the important customers or high-value items.To avoid the disclosure of privacy and economic losses,it is of great importance to fast pinpoint these blocked ones.However,existing works do not take into account the impact of the number of blocked tags on the execution time and suffer from incomplete identification of blocked tags,long identification time or privacy leakage.To overcome these limits,we propose a cross layer blocked tag identification protocol(CLBI).CLBI consists of multiple rounds,in which it enables multiple unblocked tags to select one time slot and concurrently verify them by using tag estimation in physical layer.Benefiting from the utilization of most collision slots,the execution time can be greatly reduced.Furthermore,for efficient identification of blocked tags under different proportions,we propose a hybrid protocol named adaptive cross layer blocked tag identification protocol(A-CLBI),which estimates the remaining blocked tag in each round and adjusts the identification strategy accordingly.Extensive simulations show that our protocol outperforms state-of-the-art blocked tags identification protocol.

Localization of pallets in warehouses using passive RFID system

Warehouse operation has become a critical activity in supply chain. Position information of pallets is important in warehouse management which can enhance the efficiency of pallets picking and sortation. Radio frequency identification(RFID) has been widely used in warehouse for item identifying. Meanwhile, RFID technology also has great potential for pallets localization which is underutilized in warehouse management. RFID-based checking-in and inventory systems have been applied in warehouse management by many enterprises. Localization approach is studied, which is compatible with existing RFID checking-in and inventory systems. A novel RFID localization approach is proposed for pallets checking-in. Phase variation of nearby tags was utilized to estimate the position of added pallets. A novel inventory localization approach combing angle of arrival(AOA) measurement and received signal strength(RSS) is also proposed for pallets inventory. Experiments were carried out using standard UHF passive RFID system. Experimental results show an acceptable localization accuracy which can satisfy the requirement of warehouse management.

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

The Detectability for the Myocardial Fibrosis by Tagging Imaging on Cardiovascular Magnetic Resonance

Purpose: Myocardial fibrosis causes cardiac dysfunction, arrhythmias, and sudden death. Tagging imaging on cardiovascular MR can measure the intra-myocardial motion from the dynamic deformation of lines superimposed on the myocardium. The purpose of this study was to evaluate the detectability of myocardial fibrosis using tagging imaging and to compare this with conventional cine imaging. Materials and Methods: We reviewed 4 normal control (NML) subjects, 4 patients with myocarditis (MYO), and 4 patients with old myocardial infarction (ICM). We measured circumferential strain (Ecc) from tagging imaging, and regional wall thickening (rWT) from cine imaging. Fibrosis was determined from a late gadolinium enhancement (LGE) image. We evaluate diagnostic performance by comparing values of the area under curve (AUC) using ROC analysis. Results: Mean values of Ecc and rWT decreased in the area of LGE both in MYO and ICM patients. AUC values of Ecc and rWT in all subjects were 0.98 and 0.84, respectively (p < 0.0001). These values in MYO patients were 0.95 and 0.72 (p = 0.007), respectively, and 0.99 and 0.75, respectively, in ICM patients (p = 0.0008). Conclusions: Both Ecc and rWT decreased in the area with fibrosis in the patients with MYO and ICM. Tagging imaging showed better detectability of myocardial fibrosis than did cine imaging.

A lexicalized second-order-HMM for ambiguity resolution in Chinese segmentation and POS tagging

Hidden Markov Model（HMM） is a main solution to ambiguities in Chinese segmentation anti POS （part-of-speech） tagging. While most previous works tot HMM-based Chinese segmentation anti POS tagging eonsult POS informatiou in contexts, they do not utilize lexieal information which is crucial for resoMng certain morphologieal ambiguity. This paper proposes a method which incorporates lexieal information and wider context information into HMM. Model induction anti related smoothing technique are presented in detail. Experiments indicate that this technique improves the segmentation and tagging accuracy by nearly 1%.