Multi-tasking to Address Diversity in Language Learning

With focus now placed on the learner, more attention is given to his learning style, multiple intelligence and developing learning strategies to enable him to make sense of and use of the target language appropriately in varied contexts and with different uses of the language. To attain this, the teacher is tasked with designing, monitoring and processing language learning activities for students to carry out and in the process learn by doing and reflecting on the learning process they went through as they interacted socially with each other. This paper describes a task named"The Fishbowl Technique"and found to be effective in large ESL classes in the secondary level in the Philippines.

DSP-TMM:A Robust Cluster Analysis Method Based on Diversity Self-Paced T-Mixture Model

In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture model.This model firstly adopts the t-distribution as the submodel which tail is easily controllable.On this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial parameters.After that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in training.Finally,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering methods.It provides significant guidance for the construction of the robust mixture distribution model.

What Is the Impact of Major Diversion Attitude on Undergraduates' Learning Gains?--The Mediating Role of Course Perception

Major diversion is an important part of the large-category student enrollment and training model.The degree to which undergraduates recognize the logic of the major diversion system,their satisfaction with the diverted major,and their major identity after diversion all influence their subsequent learning process and outcomes.The questionnaire survey of undergraduates in this study discovered that major diversion attitude has a significant positive effect on undergraduates'learning gains;the mediating effect test discovered that course perception plays a partially mediating role between major diversion attitude and learning gains.Therefore,under the large-category student enrollment and training model,it is necessary to improve the major diversion system in terms of formulation,major selection guidance,and major identity promotion.Furthermore,strengthening the logical connection and content coupling of different types of courses,dealing with the proportion,priority,and sequence of courses,optimizing the allocation of course resources,and reasonably planning and setting courses all play an important role in improving undergraduate learning gains.

Teaching for Learning Styles and Multiple Intelligences: a Personal Reflection

The paper reviewed what the literature has said about learning styles and multiple intelligences. By practicing a personal reflection on learning styles and multiple intelligences, the paper indicated that teachers need make paradigm shift respecting the fact that every student is gifted and can be taught with the same contents, approaches and assessment. Teaching for diversity should be implemented.

Large scale classification with local diversity AdaBoost SVM algorithm

Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.

Domain adaptive methods for device diversity in indoor localization

To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement.

Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image.The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered,and dissimilar images are separated in the low embedding space.Previous works primarily focused on defining local structure loss functions like triplet loss,pairwise loss,etc.However,training via these approaches takes a long training time,and they have poor accuracy.Additionally,representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen classes.This paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these issues.In the proposed work,class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class clusters.And other instances are treated as negatives within the same cluster.Then a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded space.The proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results(85.38%recall@1 for CARS-196%and 70.13%recall@1 for CUB-200)compared to other existing methods.

Expatriation as an Element of Diversity Management

The ongoing internationalization of companies goes hand in hand with an increase of international assignments. With it, knowledge is transferred and diverse teams emerge in the subsidiaries abroad. However, expatriation management and diversity management have been separated areas so far. Thus, the readiness to use expatriation as an integral element of an overall diversity strategy has been evaluated in an exploratory empirical study. For this purpose, semi-structured interviews have been conducted with both expatriates and HR （human resources） managers in six subsidiaries and the headquarters of an international mechanical engineering company. It was found that operative aspects of the expatriate management dominate the viewpoint of those involved. However, the findings also suggest that an implicit recognition of advantages that stem from the variety of individual employees exists. Willingness to systematically strengthen the exchange and learning process was detected. Based on these results, a new approach could be conceptualized and implemented. This provides various foci for further research.

Deep-reinforcement-learning-based water diversion strategy

Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation.Its effectiveness depends on changes in the source water and lake conditions.However,the challenge of optimizing water diversion remains because it is difficult to simultaneously improve lake water quality and minimize the amount of diverted water.Here,we propose a new approach called dynamic water diversion optimization(DWDO),which combines a comprehensive water quality model with a deep reinforcement learning algorithm.We applied DWDO to a region of Lake Dianchi,the largest eutrophic freshwater lake in China and validated it.Our results demonstrate that DWDO significantly reduced total nitrogen and total phosphorus concentrations in the lake by 7%and 6%,respectively,compared to previous operations.Additionally,annual water diversion decreased by an impressive 75%.Through interpretable machine learning,we identified the impact of meteorological indicators and the water quality of both the source water and the lake on optimal water diversion.We found that a single input variable could either increase or decrease water diversion,depending on its specific value,while multiple factors collectively influenced real-time adjustment of water diversion.Moreover,using well-designed hyperparameters,DWDO proved robust under different uncertainties in model parameters.The training time of the model is theoretically shorter than traditional simulation-optimization algorithms,highlighting its potential to support more effective decisionmaking in water quality management.

Structural diversity for decision tree ensemble learning

Decision trees are a kind of off-the-shelf predictive models, and they have been successfully used as the base learners in ensemble learning. To construct a strong classi- fier ensemble, the individual classifiers should be accurate and diverse. However, diversity measure remains a mystery although there were many attempts. We conjecture that a deficiency of previous diversity measures lies in the fact that they consider only behavioral diversity, i.e., how the classifiers behave when making predictions, neglecting the fact that classifiers may be potentially different even when they make the same predictions. Based on this recognition, in this paper, we advocate to consider structural diversity in addition to behavioral diversity, and propose the TMD （tree matching diversity） measure for decision trees. To investigate the usefulness of TMD, we empirically evaluate performances of selective ensemble approaches with decision forests by incorporating different diversity measures. Our results validate that by considering structural and behavioral diversities together, stronger ensembles can be constructed. This may raise a new direction to design better diversity measures and ensemble methods.

Catering for Learner Diversity in Hong Kong Secondary Schools:Insights from the Relationships Between Students’Learning Styles and Approaches

Purpose:Catering for learner diversity is a key issue in the recent educational reforms in Hong Kong.The present study addresses this issue through an investigation of the relationships between students’learning styles and approaches to learning in Hong Kong secondary schools.Design/Approach/Methods:A total of 6,054 junior secondary students in Hong Kong responded to a questionnaire consisting of two instruments.A series of confirmatory factor analysis,two-way analysis of variance,and structural equation modeling analysis were conducted.Findings:The results identified three types of learning style among the students which are characterized by a cognitive orientation,a social orientation,and a methodological orientation.Some significant gender-and achievement-level differences were revealed.Compared with the socially oriented learning style,the cognitively and methodologically oriented learning styles were more extensively and strongly related to students’approaches to learning,even though these students showed a greater preference for the socially oriented learning style.Originality/Value:It is unwise to blindly cater for students’learning styles in classroom teaching and curriculum design.Teachers should adopt a comprehensive and balanced approach toward the design of curriculum and teaching which not only highlights the congruence between students’learning styles and teacher’s pedagogy but also integrates the constructive frictions between them into classroom teaching.

多元生源背景下高职学生自适应学习探析

为探索高职个性化、多样化、定制化教育及提高教学改革的针对性和有效性,以某高职院校10028名在校生为研究对象,通过问卷调查开展实证研究,通过对比、关联、聚类分析和关键词词频统计分析等数据处理方式,探析多元生源背景下高职学生个性化学习表征和自适应学习模式,提出了强化职业生涯规划教育、建立健全教学管理与校企合作办学制度、优化职业教育课程体系和深化课程考核评价改革四个方面的关键举措以满足高职学生自适应学习需求。

冠状动脉CT血管造影计算FFR深度学习方法的诊断临床研究

目的本研究旨在评估冠状动脉CT血管造影计算分流量储备(fractional flow reserve,FFR)深度学习方法的诊断准确性研究,以期为临床诊治提供参考。方法这是一项单中心的前瞻性研究,63名患者参加了深度学习FFR的诊断性能评估。为了评估冠状动脉狭窄的缺血风险,提出了冠状动脉三维几何形状的自动量化方法和基于深度学习的FFR预测方法。以线状FFR为参考标准,评价深度学习FFR的诊断性能。采用受试者-操作特征曲线下面积(AUC)分析确定主要评价因子。结果对于每个患者水平,以参照FFR测量的临界值≤0.8时,深度学习FFR的AUC为0.928,缺血相关病变方面比CTA狭窄严重程度0.664表现出更高的诊断性能。深度学习FFR与FFR相关(R=0.686,P<0.001),平均差值为-0.006±0.0091(P=0.619)。二次评价的准确性、敏感性、特异性分别为87.3%、97.14%、95.45%。结论深度学习FFR是一种新的方法,可以有效地评估冠状动脉狭窄的功能意义。

水库消落带植物多样性空间格局预测模型及环境解释--基于XGBoost-SHAP模型框架

生物多样性的监测与预测对实现生物多样性保护及其可持续管理至关重要。传统方法通过实地调查来构建环境与生物多样性之间的多变量关系模型。空间大数据技术及机器和深度学习算法的发展为探索环境-生物多样性关系和预测生物多样性空间格局提供了新的视角和方法。构建了一种基于XGBoost算法的预测模型,融合实地调查的植物多样性数据和来自多源数据库的环境变量数据,分别构建了气候、地形、土壤、水文和人类活动5类共34个环境变量与植物群落物种丰富度、物种多样性和谱系多样性的关系模型,对丹江口水库消落带的植物多样性空间格局进行预测,同时结合SHAP框架确定关键环境因素;并进一步预测2050年水库消落带的植物多样性空间格局。研究表明,XGBoost算法在预测水库消落带植物多样性方面表现较好,3个多样性指标中谱系多样性的预测模型展现了最优的预测能力,而物种多样性预测模型的预测能力相对较低。结合SHAP分析发现年平均水淹时长、人类足迹与最冷季平均气温是影响消落带植物群落物种丰富度、物种多样性和谱系多样性的关键环境因素,其中年平均水淹时长的影响最为显著,随着年平均水淹时长增加,物种丰富度、物种多样性和谱系多样性降低。本研究构建的可解释预测模型可有效揭示消落带的植物多样性空间格局,为消落带生物多样性的保护和可持续管理提供科学依据,为生物多样性的监测和管理提供了新方法,对评估全球变化对生态系统的影响并促进生物多样性保护有重要意义。

战略认知差异会影响企业技术创新吗?——基于文本主题分析的实证研究

在知识经济时代,知识对创新的边际贡献超过传统物质性生产要素,企业技术创新优势更多源于管理者认知的转变,而不再由异质性的资源所决定。本文探讨了行业场景中管理者的战略认知相较于行业共享信念之间的差异对技术创新的影响,同时考虑了探索式学习与知识组合多样性在其中所发挥的路径传导作用。研究结果显示:管理者的战略认知差异能够显著促进以专利申请量为表征的技术创新数量,提高以专利外部引用为表征的技术创新质量。进一步研究发现,探索式学习和知识组合多样性在战略认知差异与技术创新的关系中发挥了部分中介作用。异质性分析发现,受到来自家族权威和行政制度的干预,管理者的战略认知差异在非家族企业与非国有企业样本中表现出更为显著的技术创新效果。研究结论从战略认知差异视角拓展了技术创新的前因研究,同时为理解中国企业的创新决策逻辑、探究创新人才培养路径提供了理论参考。

学情驱动高职院校思政课精准教学:困境、现状与策略

高职院校不利于开展精准教学的学情困境因素表现为多元化生源结构,多样性学习风格,多层次学习动力以及多水平学业能力。通过对809名高职院校大学生进行学情现状问卷调查分析,结果发现不同升学渠道生源在学习风格、课堂无聊应对策略、学习投入存在差异。根据学情困境和现状分析结果,“大历史观”教师授课逻辑、“大思政课”教育生态系统、“数字技术”分层教学模式、“情感叙事”情理交融通路是高职院校思政课精准教学的优化策略。

基于RCMDE和ISOMAP的行星齿轮传动耦合故障辨识研究

现有针对行星齿轮箱的故障诊断方法一般仅研究单一故障,但实际行星齿轮箱的故障一般由多个故障耦合而成,耦合故障的故障机理比单一故障的故障机理更复杂,振动信号中的非线性因素对特征提取的干扰更严重。针对该问题,提出了一种基于精细复合多尺度散度熵(RCMDE)、等距特征映射(ISOMAP)和遗传算法优化核极限学习机(GA-KELM)的行星齿轮箱耦合故障诊断方法。首先,利用振动加速度计采集了行星齿轮箱单一故障和耦合故障下运行时的振动信号,构建了故障数据集;随后,利用RCMDE提取了行星齿轮箱振动信号的故障特征,建立了初始的特征样本;接着,利用ISOMAP对故障特征进行了降维,并以可视化的方式获取了低维的特征样本;最后,将新特征输入至GA-KELM分类器中,对行星齿轮箱的不同故障类型进行了识别,并基于行星齿轮箱多点损伤样本,对RCMDE方法的可靠性进行了研究。研究结果表明:基于RCMDE和ISOMAP的故障特征提取方法能够有效提取振动信号中的故障特征,而GA-KELM的故障诊断准确率达到了98.13%,平均诊断准确率达到了96.25%。相较其他故障特征提取方法,基于RCMDE、ISOMAP和GA-KELM的行星齿轮箱耦合故障诊断方法能够更好地诊断行星齿轮箱的耦合故障,具有更高的诊断准确率。

Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.