Treatment of Uncertainties with Algorithms of the Paraconsistent Annotated Logic

The method presented in this work is based on the fundamental concepts of Paraconsistent Annotated Logic with annotation of 2 values (PAL2v). The PAL2v is a non-classic Logics which admits contradiction and in this paper we perform a study using mathematical interpretation in its representative lattice. This studies result in algorithms and equations give an effective treatment on signals of information that represent situations found in uncertainty knowledge database. From the obtained equations, algorithms are elaborated to be utilized in computation models of the uncertainty treatment Systems. We presented some results that were obtained of analyses done with one of the algorithms that compose the paraconsistent analyzing system of logical signals with the PAL2v Logic. The paraconsistent reasoning system built according to the PAL2v methodology notions reveals itself to be more efficient than the traditional ones, because it gets to offer an appropriate treatment to contradictory information.

Modularity-like objective function in annotated networks

We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a lin- ear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.

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.

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.

VPOT:A Customizable Variant Prioritization Ordering Tool for Annotated Variants

Next-generation sequencing(NGS) technologies generate thousands to millions of genetic variants per sample.Identification of potential disease-causal variants is labor intensive as it relies on filtering using various annotation metrics and consideration of multiple pathogenicity prediction scores.We have developed VPOT(variant prioritization ordering tool),a python-based command line tool that allows researchers to create a single fully customizable pathogenicity ranking score from any number of annotation values,each with a user-defined weighting.The use of VPOT can be informative when analyzing entire cohorts,as variants in a cohort can be prioritized.VPOT also provides additional functions to allow variant filtering based on a candidate gene list or by affected status in a family pedigree.VPOT outperforms similar tools in terms of efficacy,flexibility,scalability,and computational performance.VPOT is freely available for public use at Git Hub(https://github.com/VCCRI/VPOT/).Documentation for installation along with a user tutorial,a default parameter file,and test data are provided.

G2MF-WA:Geometric multi-model fitting with weakly annotated data

In this paper we address the problem of geometric multi-model fitting using a few weakly annotated data points,which has been little studied so far.In weak annotating(WA),most manual annotations are supposed to be correct yet inevitably mixed with incorrect ones.Such WA data can naturally arise through interaction in various tasks.For example,in the case of homography estimation,one can easily annotate points on the same plane or object with a single label by observing the image.Motivated by this,we propose a novel method to make full use of WA data to boost multi-model fitting performance.Specifically,a graph for model proposal sampling is first constructed using the WA data,given the prior that WA data annotated with the same weak label has a high probability of belonging to the same model.By incorporating this prior knowledge into the calculation of edge probabilities,vertices(i.e.,data points)lying on or near the latent model are likely to be associated and further form a subset or cluster for effective proposal generation.Having generated proposals,α-expansion is used for labeling,and our method in return updates the proposals.This procedure works in an iterative way.Extensive experiments validate our method and show that it produces noticeably better results than state-of-the-art techniques in most cases.

Ontology-Based News Linking for Semantic Temporal Queries

Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed.There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian languages.The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.Lack of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any querying.Moreover,the most straightforward search facility does not support these unindexed news resources.Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,Urdu.The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler.The corpus is then semantically linked and annotated with daily news items.Two techniques are proposed for image annotation,free annotation and fixed cross examination annotation.The second technique got higher accuracy.Build news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under it.The application is also built and linked with protégéso that the readers and journalists have an interface to query the news items directly.Similarly,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

Robust Deep Transfer Learning Based Object Detection and Tracking Approach

At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

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。

SSH架构中方法级缓存依赖的解决方案

缓存是提高系统性能的重要手段之一。实现缓存依赖就是要在被依赖对象与缓存对象之间建立一个有效关联,使数据得到合理的刷新。通过对EhCache、Map、An-notation以及AOP等相关技术的研究,提出了SSH架构中方法级依赖缓存的解决方案:使用EhCache结合Concur-rentHashMap实现缓存的细粒度化,使用Annotation存储依赖信息,通过AOP实现缓存服务代码与业务方法的分离。这种方案解决了实际应用中不能合理刷新数据、细粒度化较难等问题。实践表明,这种方案具有容易实现、便于维护和扩展、使用方便等特点。

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.

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。

论杜预《春秋经传集解》(下)

Du Yu’s Annotation of Classics and Elucidations in "Spring and Autumn Annals" is considered as a milestone for the studies of Spring and Autumn Annals, In Du’s book, the Preface to the Spring and Autumn Annals is a theoretical summary and revision for the previous studies on the Spring and Autumn Annals and the Tso Chuan. Its combination of classics and elucidations makes Tso Chuan a real elucidating study on Spring and Autumn Annals. Du expounds his philosophy and political ideas in this book.

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。

翻译中实现社会语用等效(英文)

As a medium between two different languages, between peoples of two countries, translation is not only a matter of language, but also of cross-cultural transference. According to Professor He Ziran, socio-pragmatic translation is the kind of translation which examines the conditions on language use that stem from the social and cultural situations to serve cross-cultural communication. This paper focuses on the ways used to achieve socio-pragmatic equivalence in translation practice.

两千年来古人语法分析说略(下)

It is believed that in ancient China,there were only works on graphology,phonology and critical interpretation,but no works on grammar.In works of grammar history published in modern times,they just dwell briefly on ancient grammar,and they consider that there were only some explanations of form words,a few books of form words,but no syntactic analyses.However,in fact,in ancient classical works there were abundant materials of grammatical analyses.Ancient scholars worked out quite a few grammar rules,some of which were profound and coincidental with modern grammar theories.In the present research of ancient Chinese grammar,grammatical analyses made by ancient scholars should be summed up and inherited.

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.

Paraconsistent Algorithm Extractor of Contradiction Effects―ParaExtr_(ctr)

Nowadays networks of analyses based in non-classic logics are used with success in the treatment of uncertainties. The characteristic of accepting the contradiction in his structure is the main cause of the methodologies based in Paraconsistent Logic is ideals for applications in systems of analyses and decision making. In this work we presented an algorithm based in Paraconsistent logic capable to extract in a gradual way the effects of the contradiction in originated signals of information of uncertain knowledge database. The Algorithm Paraconsistent Extractor of Contradiction effects - Paraextrctrctr is formed with base in fundamental concepts of the Paraconsistent Annotated Logic with annotation of two values (PAL2v) it can be applied in filters of networks of analyses of signal information where uncertain and contradictory signals can be present. The process of extraction of the effect of the contradiction is always begun by the largest inconsistency degree among two signals that belong to the group that is in analysis. In the end of the analysis it is found a consensus value. In this work we presented numeric example and one example of application of the Paraextrctrctr in Load Profile Forecast used in support to decision of the operation in an Electric Power System, but his application potentiality is demonstrated in several fields of the Artificial Intelligence.