颈动脉斑块MRI特征与同侧急性脑梗死患者DWIGASPECTS的相关性研究

目的:探讨颈动脉斑块MRI特征与同侧急性脑梗死患者DWI-ASPECTS的相关性。方法:回顾性搜集我院85例颈动脉斑块患者的临床及影像资料,所有患者均于出现脑血管症状2周内行颈动脉高分辨率MRI和常规颅脑MRI平扫。将患者分为DWI-ASPECTS≤7分组和DWI-ASPECTS>7分组,比较两组间临床、实验室资料和颈动脉斑块定量、定性指标的差异。通过Logistic回归筛选DWI-ASPECTS评分的预测因子。结果:25例(29.4%)DWI-ASPECTS≤7分,60例(70.6%)DWI-ASPECTS>7分。DWI-ASPECTS≤7分组患者纤维帽变薄/破裂(分别为80.0%和35.0%,P<0.001)、斑块内出血(分别为72.0%和21.7%,P<0.001)、钙化(分别为76.0%和45.0%,P=0.009)和富脂质坏死核心(分别为72.0%和45.0%,P=0.023)的发生率均高于DWI-ASPECTS>7分组;此外,DWI-ASPECTS≤7分组患者显示出更高的斑块负荷,DWI-ASPECTS≤7分组与DWI-ASPECTS>7分组的最大管壁厚度[分别为(4.38±1.68)和(3.47±1.78)mm,P=0.033]、平均管腔面积[分别为(15.33±11.57)和(22.88±13.95)mm 2,P=0.019]、最小管腔面积[分别为(11.52±11.23)和(18.98±13.52)mm 2,P=0.017]、平均管壁面积[分别为(62.81±14.32)、(55.66±14.93)mm 2,P=0.045]、最大管壁面积[分别为(66.70±14.57)、(59.56±15.19)mm 2,P=0.049]、平均标准化管壁面指数[分别为(81±14)%、(71±16)%,P=0.011]、最大标准化管壁指数[分别为(86±14)%、(76±16)%,P=0.012)]差异均有统计学意义。DWI-ASPECTS≤7分组中NIHSS评分>4分的患者比例更高(分别为48%和23.3%,P=0.025),高密度脂蛋白浓度更低[分别为(1.24±0.28)、(1.10±0.28)mmol/L,P=0.043]。Logistic回归分析结果显示,纤维帽变薄/破裂(OR=4.133,95%CI:1.151~14.836,P=0.030)和斑块内出血(OR=6.409,95%CI:1.737~23.646,P=0.005)与较低的DWI-ASPECTS评分显著相关。结论:颈动脉斑块中存在纤维帽变薄/破裂和斑块内出血的急性脑卒中患者DWI-ASPECTS评分更低,有助于评估患者的病情严重程度。

一种基于预训练模型掩码Aspect术语的数据增强方法

数据增强是解决低资源场景下数据稀缺问题的有效方案。然而,当应用于诸如方面术语提取(ATE)之类的词级别任务时,数据增强方法通常会遭受词标签不对齐的问题,从而导致效果不理想。对此提出了掩码方面语言建模(MALM)作为ATE的新型数据增强框架。为了缓解标记、标签错位问题,将ATE标签显式注入到句子上下文中,由此经过微调的MALM能够显式地调整标签信息来预测掩码的方面标记。因此,MALM可帮助生成具有新方面的高质量增强数据,提供丰富的层面方面知识。此外,提出了一个两阶段的训练策略来整合这些合成数据。通过实验,证明了MALM在两个ATE数据集上的有效性,相比基线方法,所提出的MALM有显著的性能改进。

Aspect-Level Sentiment Analysis Incorporating Semantic and Syntactic Information

Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.

A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

人工智能在急性缺血性脑卒中早期ASPECTS评估中的研究进展

急性缺血性脑卒中(AIS)的早期诊断和及时干预对于降低脑卒中的致死致残率具有重要意义。目前,临床上采用阿尔伯塔卒中项目早期计算机断层扫描评分(ASPECTS)来评估AIS的严重程度,但人为评估方法主观性过强且耗时耗力,极易导致漏诊、误诊。因此,近年来涌现了许多基于人工智能算法对AIS进行ASPECTS自动评分的方法研究。本文对此进行综述,以期为进一步研究探索提供参考。首先,简述ASPECTS评分的可靠性;其次,重点介绍目前基于人工智能的脑区提取及脑区评分的方法,证实计算机辅助ASPECTS评分能够有效提高对病情判断的可靠性;最后,总结现有ASPECTS自动评分方法存在的不足,并对其未来的发展趋势进行展望。

Aspect Extraction Approach for Sentiment Analysis Using Keywords

Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well.Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services.These reviews are also a very precious source of information for requirement engineers.But companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer reviews.Owing to this,many researchers have developed approaches for aspect-based sentiment analysis.Most existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit aspects.This paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit aspects.It also captures opinion words and classifies the sentiment about each aspect.We applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect extraction.We used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven domains.We compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.However,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline model.Pipeline solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE error.These methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.

Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.

Influence of quarantine during the coronavirus disease 2019(COVID-19)pandemic on physical and psychosocial aspects:perceptions of 214 Brazilian athletes

Background:Social distancing may affect athletes,training,causing negative effects on mental and physical health.Objective:This study therefore aimed to characterize the perception of Brazilian athletes about their physical and psychosocial aspects,sleep quality and coping strategies during the quarantine of the coronavirus disease 2019(COVID-19)pandemic.Methods:This was a cross-sectional study with online survey,performed with Brazilian athletes(amateur and professional)over 18 years.The main outcomes measures assessed were physical and psychosocial aspects,sleep quality and coping strategies.Results:A total of 214 athletes were included.The average weekly hours of training during the quarantine was 4.71±3.71 h,of which 64.5%athletes(138/214)were oriented by medical staff during training.For 52.8%(113/214)of athletes,training intensity during the quarantine was different/very different from the intensity before the quarantine.79.4%athletes(170/214)reported moderate to extreme difficulties in keeping the same level of training during the quarantine.77.1%athletes(165/214)had moderate to extreme anxiety and each of the athletes had concern about his or her athletic career future,including return to the sport.72.9%athletes(156/214)reported change in sleep schedule during the quarantine period.Conclusion:The quarantine period during COVID-19 pandemic negatively affected the athlete^perception about training routine,since athletes reported reduction in training hours and training intensity.Overall,the athletes reported that they were moderately to extremely anxious.They also had concerns about their career in the future,as well as concerns regarding return to sport.

面向Aspect的操作系统研究

面向Aspect软件设计是一种新的软件设计思想和技术.分析了近年来操作系统贯穿特性与Aspect概念,构件重构、系统演化与设计,系统安全、性能检测与容错这3个方面的研究成果,指出面向Aspect操作系统研究已经获得了积极的成果.但是,目前的研究缺乏一定的深度和广度,尚没有在操作系统的设计阶段运用AOP(Aspect-Orientedoperating)思想的成果出现.在已有操作系统代码中抽象Aspect的过程中,缺乏完整的工程化和规范化的研究.这些问题的解决有赖于面向Aspect研究的进一步深入.最后,对面向Aspect操作系统研究的前景进行展望,认为有关AOSD(Aspect-Orientedsoftwaredevelopment)的研究有可能对未来操作系统的发展产生重大影响.

Biermer’s Disease at the Donka National Hospital in Guinea—Epidemio-Clinical, Therapeutic and Evolutionary Aspect in the Internal Medicine Department

Introduction: Biermer’s disease is an autoimmune disease characterized by a lack of absorption of vitamin B12 in connection with the production of antibodies (A) destroying the intrinsic factor (IF) which allows the absorption of vitamin B12 (cobalamin). These clinical manifestations are polymorphic and severe in our context. The objective of this work is to identify the epidemiological-clinical, therapeutic and evolutionary characteristics of Biermer’s disease in Guinean population. Materials and methods: This was a retrospective of patient files followed for Biermer’s disease at the internal medicine department of Donka National Hospital from January 2012 to December 2021. Results: Eight patients were included including 5 women and 3 men. The average age of the patients was 48 years old. The diagnostic delay was 3.6 years on average. All our patients had bioclinical anemia (8 cases, i.e. 100%) followed by epigastralgia in 4 cases (50%), neurological damage such as sensitive polyneuropathy in 3 cases (37.5%). Four patients had acquired melanoderma (50%). Hypovitaminosis B12 was found in 4 patients. The myelogram performed in three patients (37.5%) found medullary megaloblastosis. One patient had Hashimoto’s disease associated with Biermer’s disease in endoscopy, (FOGD) found fundica trophy on macroscopy in 4 cases (50%). Treatment consisted of B12 vitamin therapy in all cases with a favorable clinical and biological outcome. Conclusion: Biermer’s disease remains common in Africa and is characterized at a younger age in addition to the severity of clinical and biological manifestations. The care consists of taking vitamin B12 which remains accessible in our context.

A Knowledge-Integrate Cross-Domain Data Generation Method for Aspect and Opinion Co-Extraction

To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.

Sustainability as an Ethical Aspect of the Theory-Practice Gap in Business Schools

This paper aims to reframe sustainability as an ethical aspect of the theory-practice gap in business and management education for sustainable development,which should be viewed as an integral part of knowledge produced and disseminated in business schools.The paper adopts a narrative approach to review the relevant literature on two streams of research,namely,the theory-practice gap and sustainability in reforming business schools.The synthesis and discussion of the existing literature suggest that while sustainability is frequently viewed with an ethical sentiment,the existing research overlooks its significance in bringing together knowledge and practice in business schools.This paper highlights the potential of sustainability as a theoretical lens in bridging the theory-practice gap in business schools;proposing to rethink the conceptual space that lies in ethics for further theoretical developments.The author urges business and management scholars to engage in burgeoning debates on business school reforms relating to the theory-practice gap and sustainability with an emphasis on ethics.The author contends that the neglected theoretical linkages between the theory-practice gap and sustainability provide fruitful directions for future research.Through a moral lens,business schools can move toward responsible management education for a more sustainable future.

Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

Aspect’s extraction is a critical task in aspect-based sentiment analysis,including explicit and implicit aspects identification.While extensive research has identified explicit aspects,little effort has been put forward on implicit aspects extraction due to the complexity of the problem.Moreover,existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’dependency problems.Therefore,in this paper,a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed.The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF(Bidirectional Long Short Memory-Conditional Random Field),which serve as a memory to process dependent sentences to infer implicit aspects.It can identify implicit aspects from four types of sentences,including independent and three types of dependent sentences.The study is evaluated on a largemovie reviews dataset with 50k examples.The experimental results showed that the explicit aspect identification method achieved 89%F1-score and implicit aspect extraction methods achieved 76%F1-score.In addition,the proposed approach also performs better than the state-of-the-art techniques(NMFIAD andML-KB+)on the product review dataset,where it achieved 93%precision,92%recall,and 93%F1-score.

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key task.Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature.Rule based approaches,like dependency-based rules,are quite popular and effective for this purpose.However,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency parser.Another popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s behavior.However,in general,the sequential rule-based approaches have poor generalization capability.Moreover,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb aspects.In this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects.Additionally,after rigorous analysis,we have also constructed rules for the extraction of verb aspects.These verb rules primarily based on the association between verb and opinion words.The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets.The F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing approaches.These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

面向Aspect的程序设计——一种新的编程范型

1引言 面向Aspect的程序设计(Aspect Oriented Program-ming:AOP)[1],其概念的出现不过几年的时间,却体现了解决问题的非常简单而深刻的"分而治之"的思想.