Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Improved genome annotation of Brassica oleracea highlights the importance of alternative splicing

Brassica oleracea has been developed into many important crops,including cabbage,kale,cauliflower,broccoli and so on.The genome and gene annotation of cabbage(cultivar JZS),a representative morphotype of B.oleracea,has been widely used as a common reference in biological research.Although its genome assembly has been updated twice,the current gene annotation still lacks information on untranslated regions(UTRs)and alternative splicing(AS).Here,we constructed a high-quality gene annotation(JZSv3)using a full-length transcriptome acquired by nanopore sequencing,yielding a total of 59452 genes and 75684 transcripts.Additionally,we re-analyzed the previously reported transcriptome data related to the development of different tissues and cold response using JZSv3 as a reference,and found that 3843 out of 11908 differentially expressed genes(DEGs)underwent AS during the development of different tissues and 309 out of 903 cold-related genes underwent AS in response to cold stress.Meanwhile,we also identified many AS genes,including BolLHCB5 and BolHSP70,that displayed distinct expression patterns within variant transcripts of the same gene,highlighting the importance of JZSv3 as a pivotal reference for AS analysis.Overall,JZSv3 provides a valuable resource for exploring gene function,especially for obtaining a deeper understanding of AS regulation mechanisms.

Meibomian glands segmentation in infrared images with limited annotation

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of these folders deliver relevant indexing information.From the outcomes,it is dif-ficult to discover data that the user can be absorbed in.Therefore,in order to determine the significance of the data,it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image annotation.Initially,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are labelled.The proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_Measure.With the assistance of the pro-posed methodology,the unlabeled images are labelled.

基于Annotation的Spring AOP系统设计

简要介绍AOP(Aspect Oriented Programming)技术和Annotation技术,讨论了Spring中的AOP的两种实现。通过同一个案例阐述了在Spring中通过XML配置来实现AOP和使用了Annotation在Spring中实现AOP,比较了两种实现方式的优缺点。对在Spring使用Annotation实现AOP作了总结,从而体现了Annotation在设计系统时其简洁性和良好的扩展性。

基于XML和Annotation的异构系统数据集成方式的研究

论文运用XML的强大数据表达能力以及J2EE的反射以及Annotation编程来实现异构系统数据集成方式。该方式通过对原始结构代码进行简单的标记,将不同的数据结构串联起来。同时在转换的过程中将细节屏蔽起来并封装成标准接口,用户只需要将提供输入以及所需要的数据结构类型就可以方便地进行转换。最后论文给出集成系统详细的设计方法。

Product image sentence annotation based on kernel descriptors and tag-rank

Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.

基于Annotation的Spring AOP日志处理的设计与实现

系统维护和错误排查需要程序能自动的输出日志信息,将用户的操作记录下来。而日志本身不太可能是你开发的主程序的主要任务。如果能将"不可见的"、通用的日志代码注入主程序中,而保证自身业务逻辑的纯洁性,降低代码之间的耦合度,增加程序的灵活性和可重用性。本文使用Spring AOP的Annotation方式,写一个切面,并且指定切入的范围(切入点),当系统运行后,日志信息将可以自动输出或记录在指定的文件及数据库中。

基于Annotation的Spring事务应用设计

本文简要介绍Spring的事务实现技术,讨论了Spring中的事务的两种实现方式。通过同一个案例阐述了在Spring中通过xml配置来实现事务和使用了Annotation实现事务,比较了两种实现方式的优缺点。

基于Annotation数据结构自动生成的研究与实现

为了使整个数据库设计更加工程化、规范化和便捷化,使用Annotation注解技术实现数据库物理结构的自动生成,使之从数据库设计文档自动生成关系型数据库结构代码的方法,从而大大缩减了相关数据库开发的周期,提高了数据库开发的效率.

用Annotation简化Java程序的开发

简要介绍了Annotation技术本身的实现原理,并通过具体实例阐述了如何在软件开发中使用Annotation技术来简化Java程序开发的复杂性,提高程序的可读性和易用性。同时归纳了Annotation技术与XML技术各自的优缺点及适用范围。

基于Annotation的Spring事务应用设计

该文简要介绍Spring的事务实现技术,讨论了Spring中的事务的两种实现方式。通过同一个案例阐述了在Spring中通过xml配置来实现事务和使用了Annotation实现事务,比较了两种实现方式的优缺点。对在Spring使用Annotation实现事务作了总结,从而体现使用Annotation实现事务简洁性和良好的扩展性。

Comprehensive annotation of the Chinese tree shrew genome by large-scale RNA sequencing and long-read isoform sequencing

The Chinese tree shrew(Tupaia belangeri chinensis)is emerging as an important experimental animal in multiple fields of biomedical research.Comprehensive reference genome annotation for both mRNA and long non-coding RNA(lncRNA)is crucial for developing animal models using this species.In the current study,we collected a total of 234 high-quality RNA sequencing(RNA-seq)datasets and two long-read isoform sequencing(ISO-seq)datasets and improved the annotation of our previously assembled high-quality chromosomelevel tree shrew genome.We obtained a total of 3514 newly annotated coding genes and 50576 lncRNA genes.We also characterized the tissuespecific expression patterns and alternative splicing patterns of mRNAs and lncRNAs and mapped the orthologous relationships among 11 mammalian species using the current annotated genome.We identified 144 tree shrew-specific gene families,including interleukin 6(IL6)and STT3 oligosaccharyltransferase complex catalytic subunit B(STT3B),which underwent significant changes in size.Comparison of the overall expression patterns in tissues and pathways across four species(human,rhesus monkey,tree shrew,and mouse)indicated that tree shrews are more similar to primates than to mice at the tissue-transcriptome level.Notably,the newly annotated purine rich element binding protein A(PURA)gene and the STT3B gene family showed dysregulation upon viral infection.The updated version of the tree shrew genome annotation(KIZ version 3:TS_3.0)is available at http://www.treeshrewdb.org and provides an essential reference for basic and biomedical studies using tree shrew animal models.

A Method of Gene-Function Annotation Based on Variable Precision Rough Sets

It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.

Author Correction:Updated annotation of the wild strawberry Fragaria vesca V4 genome

Since the publication of this article,the authors have noticed that the GeneIDs from new and original genome annotations don’t match in Table S6,the correct Table S6 is given here.The authors would like to apologize for this error.

Automatic Placement of GIS Vector Map Annotation in Area Feature by Long Diagonal

This paper discusses the placement of Chinese annotation from point of view of graphics. Area Feature is classified as simple polygon, complex polygon and special polygon. For simple ones, annotations are placed along the longest edge. For complex ones, firstly the polygon are simplified according to close points, then the longest diagonal is gotten by comparing length, lastly, annotations are placed along long diagonal. For special ones, the polygon are partitioned into several parts by a certain rule for getting their sub\|diagonals, then their annotation are placed by means of the second.

Bridge Ontology: A Multi-Ontologies-Based Approach for Semantic Annotation

Representing the relationships between ontologies is the key problem of semantic annotations based on multi-ontologies. Traditional approaches only had the ability of denoting the simple concept subsumption relations between ontologies. Through analyzing and classifying the relationships between ontologies, the idea of bridge ontology was proposed, which had the powerful capability of expressing the complex relationships between concepts and relationships between relations in multi-ontologies. Meanwhile, a new approach employing bridge ontology was proposed to deal with the multi-ontologies-based semantic annotation problem. The bridge ontology is a peculiar ontology, which can be created and maintained conveniently, and is effective in the multi-ontologies-based semantic annotation. The approach using bridge ontology has the advantages of low-cost, scalable, robust in the web circumstance, and avoiding the unnecessary ontology extending and integration. Key words semantic web - bridge ontology - multi-ontologies - semantic annotation CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024). National Grand Fundamental Research 973 Program of China (2002CB312000), National Re-search Foundation for the Doctoral Program of Higher Education of China (20020286004)Biography: WANG Peng (1977-), male, Ph.D candidate, research direction: semantic web, ontology, and knowledge representation on the Web.

Ontology-Based Semantic Annotation for Problem Set Archives in the Web

Aimming at the difficulty in getting semantic informarton from each problem in problem set archives, We propose a new method of ontology based semantic annotation for problem set archives, which utilizes programming knowledge domain ontology to add semantic annotations to problems in the Web. The system we developed adds semantic annotation for each problem in the form of Extensible Makeup Language. Our method overcomes the difficulty of extracting semantics from problem set archives and the efficiency of this method is demonstrated through a case study. Having semantic annotations of problems, a student can efficiently locate the problems that logically corre spond to his knowledge.

Semantic image annotation based on GMM and random walk model

Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.