The Relationship of Effortful Control to Academic Achievement via Children’s Learning-Related Behaviors

Effortful control (EC) is a temperamental self-regulatory capacity, defined as the efficiency of executive attention [1], which is related to individual differences in self-regulation. Although effortful control covers some dispositional self-regulatory abilities important to cope with social demands of successful adaptation to school, such as attention regulation, individual differences in EC have recently been associated with school functioning through academic achievement including the efficient use of learning-related behaviors, which have been found to be a necessary precursor of learning and they refer to a set of children’s behaviors that involve organizational skills and appropriate habits of study. Therefore, the aim of this study is to review the literature on EC’s relationship to academic achievement via learning-related behaviors, which reflect the use of metacognitive control processes in kindergarten and elementary school students. The findings indicate that EC affects academic achievement through the facilitation of the efficient use of metacognitive control processes.

Study on Self-consciousness of Children With Learning Disabilities and Related Factors

Objective To study the self-consciousness of children with learning disabilities (LD) and to identify related factors. Methods Five hundred and sixty pupils graded from 1 to 6 in an elementary school were investigated. According to the pupil rating scale revised screening for learning disabilities (PRS), combined Raven’s test (CRT) and achievement of main courses, 35 of 560 pupils were diagnosed as LD children. Thirty-five children were selected from the average children and 35 from advanced children in academic achievement equally matched in class, gender, and age with LD children as control groups. The three groups were tested by Piers-Harris children’s self-concept scale. Basic information of each subject was collected by self-made questionnaire. Results Compared with the average and advanced children, LD children got significantly lower scores in self-concept scale. Based on logistic regression analysis, 3 factors were identified, including family income per month, single child and delivery model. Conclusion The results suggest that self-consciousness of children with LD is lower than that of normal children.

Flooding and its relationship with land cover change, population growth, and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve—receiver operating characteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC>90%)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the period between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72%between 2000 and 2017,while the PG rate increased by 51.68%over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.

Relational Reinforcement Learning with Continuous Actions by Combining Behavioural Cloning and Locally Weighted Regression

Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.

基于时频特征融合和关系网络的少样本轴承故障诊断方法研究

针对滚动轴承故障样本不足和特征信息获取不全面导致故障诊断准确率低的问题,提出了一种基于时频特征融合和关系网络的少样本故障诊断方法。该方法结合元学习的训练策略,首先设计了一个特征提取模块,用于获取滚动轴承振动信号的时频域信息并进行融合,以此加强获取特征的全面性;其次使用关系网络的度量模块计算支持样本和查询样本的相似得分,最终实现故障诊断。实验结果表明,在CWRU数据集的跨工况场景下,本方法展现出了优异的性能,故障诊断准确率最高可达99.82%,并有效验证了特征提取模块的有效性,显著提升了滚动轴承故障诊断的准确性和可靠性。

An Exploration of the Concept of Learner Autonomy

The concept of learner autonomy is well known as very complex as many scholars put emphasis on differences between his ideas and others'. That no consensus has reached so far puzzles us. However, a good understanding of autonomy is the basis of putting development of autonomy into practice. Through examining common features of different concepts and explaining the relationship between autonomy and its related concepts, the highly condensed qualities of "relativity" and "duality" are put forward and some implications about specific ways of fostering autonomy in Chinese educational context are also discussed.

Event temporal relation computation based on machine learning

Temporal relation computation is one of the tasks of the extraction of temporal arguments from event, and it is also the ultimate goal of temporal information processing. However, temporal relation computation based on machine learning requires a lot of hand-marked work, and exploring more features from discourse. A method of two-stage machine learning based on temporal relation computation （TSMLTRC） is proposed in this paper for the shortcomings of current temporal relation computation between two events. The first stage is to get the main temporal attributes of event based on classification learning. The second stage is to compute the event temporal relation in the discourse through employing the result of the first stage as the basic features, and also employing some new linguistic characteristics. Experiments show that, compared with the artificial golden rule, the computational efficiency in the first stage is much higher, and the F1-Score of event temporal relation which is computed through combining multi-features may be increased at 85.8% in the second stage.

Deep learning-based intelligent management for sewage treatment plants

It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.

Lexicalized Dependency Paths Based Supervised Learning for Relation Extraction

Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in adependency tree which we call lexicalized dependency paths (LDPs). We showthat this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviatethe data sparsity problem. We apply lexicalized dependency paths to supervisedlearning using the ACE corpus and show that it can achieve similar performancelevel to other state-of-the-art methods and even surpass them on severalcategories.

Relative attribute based incremental learning for image recognition

In this study, we propose an incremental learning approach based on a machine-machine interaction via relative attribute feedbacks that exploit comparative relationships among top level image categories. One machine acts as ＇Student （S）＇ with initially limited information and it endeavors to capture the task domain gradually by questioning its mentor on a pool of unlabeled data. The other machine is ＇Teacher （T）＇ with the implicit knowledge for helping S on learning the class models. T initiates relative attributes as a communication channel by randomly sorting the classes on attribute space in an unsupervised manner. S starts modeling the categories in this intermediate level by using only a limited number of labeled data. Thereafter, it first selects an entropy-based sample from the pool of unlabeled data and triggers the conversation by propagating the selected image with its belief class in a query. Since T already knows the ground truth labels, it not only decides whether the belief is true or false, but it also provides an attribute-based feedback to S in each case without revealing the true label of the query sample if the belief is false. So the number of training data is increased virtually by dropping the falsely predicted sample back into the unlabeled pool. Next, S updates the attribute space which, in fact, has an impact on T＇s future responses, and then the category models are updated concurrently for the next run. We experience the weakly supervised algorithm on the real world datasets of faces and natural scenes in comparison with direct attribute prediction and semi-supervised learning approaches, and a noteworthy performance increase is achieved.

reducing pre-school education norma 1 school students' affective filter in english learning though cooperative learning

during the past twenty year,many of the major developments in language teaching in some way related to the need to acknowledge affective factors in language learning.

Research on Statistical Relational Learning and Rough Set in SRL

Statistical relational learning constructs statistical models from relational databases, combining relational learning and statistical learning. Its strong ability and special property make statistical relational learning become one of the important areas in machine learning research.In this paper,the general concepts and characters of statistical relational learning are presented firstly.Then some major branches of this newly emerging field are discussed,including logic and rule-based approaches,frame and object-oriented approaches,functional programming-based approaches.After that several methods of applying rough set in statistical relational learning are described,such as gRS-ILP and VPRSILP. Finally some applications of statistical relational leaning are briefly introduced and some future directions of statistical relational learning and the application of rough set in this area are pointed out.

Some Important Relations in College English Teaching

There are some important relations in College English teaching.How to deal with these relations is of great significance for efficient teaching and learning.The paper takes three key relations,i.e.,the relation between language competence and communicative competence,one’s mother tongue and foreign languages,foreign language competence and quality-oriented education,as research objects,and has made a detailed analysis of the relationship between the two elements in each pair.The paper points out that only if teachers know the relations between them clearly can they teach better.

An Exploration of Key Features of Major-Country Diplomacy with Chinese Characteristics

China,under the guidance of Xi Jinping Thought on Diplomacy,is making all-round efforts to pursue major-country diplomacy with Chinese characteristics and has achieved a series of historical achievements.Major-country diplomacy with Chinese characteristics significantly diverges from conventional Western major-country diplomacy.It is characterized by a vision that prioritizes national rejuvenation,aims to advance human civilizations,emphasizes the establishment of a new type of international relations,and strives for the building of a human community with a shared future.

基于自适应上下文匹配网络的小样本知识图谱补全

知识图谱在构建过程中需要面对繁杂的现实世界信息,无法建模所有知识,因此需要补全。真实的知识图谱中很多类型的关系通常只有少量的训练实体样本对。因此,如何进行小样本知识图谱补全是一个十分有价值的问题。目前基于嵌入的方法一般通过注意力机制等方法聚合实体上下文信息,通过学习关系嵌入的方式来补全知识图谱,仅考虑关系层面的匹配程度,虽然能够预测未知关系,但往往准确度不高。针对小样本知识图谱补全问题,提出了一个自适应上下文匹配网络(Adaptive Context Matching Network,ACMN)。首先提出一个共性邻居感知编码器,聚合参考集实体上下文,即一跳邻居实体,获得共性邻居感知编码;接着提出一个任务相关实体编码器,挖掘任务实体上下文与共性上下文的相似度信息,区分一跳邻居对当前任务的贡献,增强实体表征;然后提出一个上下文关系编码器获得动态关系表征;最后通过加权求和综合考虑实体上下文和关系的匹配程度,完成补全。ACMN从实体上下文相似度和关系匹配程度两个方面综合评价查询三元组是否成立,能够在小样本的背景下有效提高预测准确性。在两个公共数据集上和其他8个广泛使用的算法进行比较,ACMN在不同规模的小样本情况下,取得了目前最好的补全结果。

AMFRel:一种中文电子病历实体关系联合抽取方法

中文电子病历实体关系抽取是构建医疗知识图谱,服务下游子任务的重要基础。目前,中文电子病例进行实体关系抽取仍存在因医疗文本关系复杂、实体密度大而造成医疗名词识别不准确的问题。针对这一问题,提出了基于对抗学习与多特征融合的中文电子病历实体关系联合抽取模型AMFRel(adversarial learning and multi-feature fusion for relation triple extraction),提取电子病历的文本和词性特征,得到融合词性信息的编码向量;利用编码向量联合对抗训练产生的扰动生成对抗样本,抽取句子主语;利用信息融合模块丰富文本结构特征,并根据特定的关系信息抽取出相应的宾语,得到医疗文本的三元组。采用CHIP2020关系抽取数据集和糖尿病数据集进行实验验证,结果显示:AMFRel在CHIP2020关系抽取数据集上的Precision为63.922%,Recall为57.279%,F1值为60.418%;在糖尿病数据集上的Precision、Recall和F1值分别为83.914%,67.021%和74.522%,证明了该模型的三元组抽取性能优于其他基线模型。

融合选择注意力的小样本知识图谱补全模型

在面对实体对关系复杂或目标邻域稀疏等情况时,现有的小样本知识图谱补全模型普遍存在关系表示学习能力不足以及忽略实体对相对位置和交互作用的问题。基于此,提出一种基于选择注意力机制和交互感知的小样本知识图谱补全模型(SAIA)。首先,通过在聚合邻域信息过程中引入选择注意机制,帮助邻域编码器聚焦更重要的邻居以减少噪声邻居的不良影响;其次,在关系表示学习阶段,利用背景知识图谱中与任务关系相关的信息学习更加准确的关系表示;最后,为了挖掘知识图谱实体之间的交互信息和位置信息,设计了一个实体对公共交互率指标(CIR)来衡量实体对三阶路径内的关联程度,然后结合实体语义信息共同预测新的事实。实验结果表明该方法优于目前最先进的小样本知识图谱补全模型。与基准模型最优的结果相比,SAIA在NELL-one和Wiki-one数据集上的5-shot链接预测中,平均倒数排名(MRR)、Hits@10、Hits@5以及Hits@1等性能评价指标分别提高了0.038、0.011、0.028和0.052以及0.034、0.037、0.029和0.027,验证了所提模型的有效性和可行性。

基于灰色关联度分析-极限学习机的低阻油层及水淹层测井识别——以渤海P区块馆陶组为例

历经近20的开发,渤海P区块进入高含水期,馆陶组发育的大量低阻油层与水淹层在测井曲线形态上差异不明显。为了精确进行水淹层识别以及水淹层等级划分,采用了机器学习算法。首先采用灰色关联度分析,筛选低阻油层和水淹层识别的敏感参数曲线;其次构建了极限学习机水淹层识别模型,对模型进行训练,获取最优参数。将其应用于实际资料处理,结果表明,基于灰色关联度分析极限学习机的低阻油层及水淹层测井识别方法对低阻油层与水淹层的预测精度较高,符合率达89.3%,远远优于未经过灰色关联度分析筛选的预测结果,具有实际应用价值。