Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration

In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.

An Ensemble Learning Model for Early Dropout Prediction of MOOC Courses

Massive open online courses(MOOCs)have become a way of online learning across the world in the past few years.However,the extremely high dropout rate has brought many challenges to the development of online learning.Most of the current methods have low accuracy and poor generalization ability when dealing with high-dimensional dropout features.They focus on the analysis of the learning score and check result of online course,but neglect the phased student behaviors.Besides,the status of student participation at a given moment is necessarily impacted by the prior status of learning.To address these issues,this paper has proposed an ensemble learning model for early dropout prediction(ELM-EDP)that integrates attention-based document representation as a vector(A-Doc2vec),feature learning of course difficulty,and weighted soft voting ensemble with heterogeneous classifiers(WSV-HC).First,A-Doc2vec is proposed to learn sequence features of student behaviors of watching lecture videos and completing course assignments.It also captures the relationship between courses and videos.Then,a feature learning method is proposed to reduce the interference caused by the differences of course difficulty on the dropout prediction.Finally,WSV-HC is proposed to highlight the benefits of integration strategies of boosting and bagging.Experiments on the MOOCCube2020 dataset show that the high accuracy of our ELM-EDP has better results on Accuracy,Precision,Recall,and F1.

Achieve Intended Learning Outcomes and Improving Digital Literacy Skills for Practical-Based Subjects Using Online Teaching via Propagation of OER Materials

As professors are subjected to teaching their classes online due to the recent COVID-19, our local Hong Kong students find it difficult to consult their teachers, and ultimately would fail to achieve the intended learning outcomes, especially for practical-based subjects. In this research, students having online classes of a practical-based fabric design subject were encouraged to self-study from Open Educational Resource (OER) materials for a further and better understanding of their subject. Additionally, online materials were developed to improve students’ understanding via skill of digital literacy. Their learning progress was evaluated and compared to the face-to-face version. The majority of students found online classes combined with self-studying OER materials, potentially be a substitute for face-to-face classes. Most of the students further opined different OER videos assisted them without any face-to-face instructions in practical works, to develop new fabric samples from the inspiration. Analysis of test results, and comparison of students’ final grades with different learning modes, supported these phenomena.

Quality Models for Open, Flexible, and Online Learning

This article is based on research conducted for the European CommissionEducation & Training 2020 working group on digital and online learning(ET2020 WG-DOL) specifically regarding policy challenges, such as thefollowing: 1) Targeted policy guidance on innovative and open learningenvironments under outcome;2) Proposal for a quality assurance modelfor open and innovative learning environments, its impact on specificassessment frameworks and its implication for EU recognition and transparencyinstruments. The article aims to define quality in open, flexible,and online learning, particularly in open education, open educationalresources (OER), and massive open online courses (MOOC). Hence,quality domains, characteristics, and criteria are outlined and discussed,as well as how they contribute to quality and personal learning so thatlearners can orchestrate and take responsibility for their own learningpathways. An additional goal is to identify the major stakeholders directlyinvolved in open online education and to describe their visions, communalities,and conflicts regarding quality in open, flexible, and online learning.The article also focuses on quality in periods of crisis, such as duringthe pandemic in 2020. Finally, the article discusses the rationale and needfor a model of quality in open, flexible, and online learning based on threemajor criteria for quality: excellence, impact, and implementation fromthe learner’s perspective.

Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

Using the Technology Acceptance Model to Analyze the Learning Outcome of Open Education Resources

Along with the development of information and communications technology,open educational resources were widely applied in training usage.The use of these resources facilitates the access to knowledge by enabling learners to transcend time and space.In this way,learners are able to obtain new knowledge more actively and efficiently than before.Using Technology Acceptance Model(TAM)as the theoretical foundation,this study aims to explore the learning outcome of using open educational resources with the perceived convenience as the external variable.In this study,the open educational resources were defined as online courses on the Open Course Ware(OCW)and Massive Open Online Courses(MOOCs),on which the learners choose courses themselves and study without the impact from people,matters,time,space,and things with the help of the Internet.To achieve the objectives of the study,the researchers conducted a survey with the participants who had already used the open educational resources.In total,124 valid samples were collected.The Partial Least Squares(PLS)statistical method was used to carry out the analysis.Overall,the model of this study has good prediction and explanatory power.After the data analysis,the study found that the perceived convenience exerts a positive impact on the use of the open educational resources.In addition,among the four TAM variables,the perceived usefulness does not exert a significant impact on the behavioral intention to use,but the other three TAM variables all have a significant impact on the behavioral intention.

Human Rights and Social Justice through Open Educational Resources and Lifelong Learning

A landmark in the realization of UNESCO’s Sustainability Goals,Education for All(SDG4),was passed when the organization’s Recommendation of Open Educational Resources(OER)was uniformly adopted in 2019.Now it is time to transfer from the consciousness of OER to their mainstream realization at all levels,micro,meso,and macro,including all stakeholders,such as governments,institutions,academics,teachers,administrators,librarians,students,learners,and the civil service.The OER Recommendation includes five areas:building capacity and utilizing OER;developing supportive policies;ensuring effectiveness;promoting the creation of sustainable OER models;promoting and facilitating international collaboration;monitoring and evaluation.OER are valued as a catalyst for innovation and the achievement of UNESCO’s SDG 4,education for all,lifelong learning,social justice,and human rights.The OER Recommendation will be a catalyst for the realization of several other SDGs.Because access to quality OER concerns human rights and social justice,this Recommendation is vital.In 2020,the effects of the worldwide COVID-19 pandemic clearly demonstrated the importance of opening up education and the access to internationally recognized,qualified learning resources.This article describes and discusses how the promise of resilient,sustainable quality open education can be fulfilled in the new normal and the next normal.

Attitude, Perception and Use of E-Learning at Open University Malaysia

To ensure the success of the e-learning initiatives,OUM has developed its own e-learning management system,known as myLMS.Since its introduction,many modifications and improvements have been introduced to increase its effectiveness.It is now timely that OUM take stock of its students'attitudes towards e-learning.Thus,a survey was conducted on about 1,000 students at one of OUM's own learning centres,that is,the Kelantan Regional Centrel.The study indicated that generally the teacher cohort had a somewhat neutral attitude towards e-learning.The use of e-learning was more specifically aimed at achieving short term goals of obtaining good coursework and examination grades by capitalizing on the use of the Discussion Board and Courseware.A closer examination reveals that the females prefer the Discussion Board while the males prefer the Courseware.Learners in the Engineering and English programmes had more positive attitudes towards e-learning compared to learners in the Mathematics and Science programme. Learners with CGPA＞3.0 who are categorized as high achievers are more positive towards e-learning as compared to the low achievers(CGPA＜3.0).Age difference,learners‘income per month,learners’Internet and e-learning habits were also found to be predictors of attitude towards e-learning.

OpenLearn:一个可持续的开放学习系统

以英国开放大学的OpenLearn学习平台为对象,从生态学的视角分析了OpenLearn的生态系统构成,并重点论述了该生态系统的可持续发展问题,最后分析了OpenLearn对于开放学习可持续发展的启示与反思。

E-Learning环境下信息资源组织发展趋势

根据E-Learning环境注重用户个性化需求的新特点、新技术,分析了E-Learning环境下信息资源组织的重要性,指出应用Web 2.0及可视化的理念和技术进行信息资源组织、以开放教育资源的形式进行信息资源组织和发布、以门户的方式进行资源整合是信息资源组织的发展趋势。

Open Learn项目对我国远程教育优质资源共建共享的启示

针对我国远程教育优质资源共建共享中存在的问题,分析并介绍了英国开放大学Open Learn项目中优质资源共建共享的一些做法,提出了其优质教学资源共建共享在理念、方法以及途径等方面带给我们的一些启示。

国外E-learning内涵的嬗变与趋势

E-learning概念在学术和社会范围内广泛运用。这一体系随着科学技术的发展,内涵在随之演变。在当今的工作、互动、协调、社会化和学习活动中,E-learning起到了一个很重要作用。本文分两部分论述了E-learning内涵的嬗变,以及审视这些概念的重合和差异,探究其趋势。

Establishment of a prediction tool for ocular trauma patients with machine learning algorithm

AIM:To predict final visual acuity and analyze significant factors influencing open globe injury prognosis.METHODS:Prediction models were built using a supervised classification algorithm from Microsoft Azure Machine Learning Studio.The best algorithm was selected to analyze the predicted final visual acuity.We retrospectively reviewed the data of 171 patients with open globe injury who visited the Pusan National University Hospital between January 2010 and July 2020.We then applied cross-validation,the permutation feature importance method,and the synthetic minority over-sampling technique to enhance tool performance.RESULTS:The two-class boosted decision tree model showed the best predictive performance.The accuracy,precision,recall,F1 score,and area under the receiver operating characteristic curve were 0.925,0.962,0.833,0.893,and 0.971,respectively.To increase the efficiency and efficacy of the prognostic tool,the top 14 features were finally selected using the permutation feature importance method:(listed in the order of importance)retinal detachment,location of laceration,initial visual acuity,iris damage,surgeon,past history,size of the scleral laceration,vitreous hemorrhage,trauma characteristics,age,corneal injury,primary diagnosis,wound location,and lid laceration.CONCLUSION:Here we devise a highly accurate model to predict the final visual acuity of patients with open globe injury.This tool is useful and easily accessible to doctors and patients,reducing the socioeconomic burden.With further multicenter verification using larger datasets and external validation,we expect this model to become useful worldwide.

Adaptive adjustment of iterative learning control gain matrix in harsh noise environment

For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent： the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.

Dynamical learning of non-Markovian quantum dynamics

We study the non-Markovian dynamics of an open quantum system with machine learning.The observable physical quantities and their evolutions are generated by using the neural network.After the pre-training is completed,we fix the weights in the subsequent processes thus do not need the further gradient feedback.We find that the dynamical properties of physical quantities obtained by the dynamical learning are better than those obtained by the learning of Hamiltonian and time evolution operator.The dynamical learning can be applied to other quantum many-body systems,non-equilibrium statistics and random processes.

在中心城市广播电视大学中开展M-Learning的可行性研究

该文通过对中心城市电视大学的发展背景及远程开放教育现状的分析，认为在中心城市的广播电视大学中开展移动学习M—Learning，将能有效地提高远程开放教育的教学效果和学习支持服务，并以此增强电大系统在现代远程教育市场中的竞争力。

LeNS China:A Chinese learning network on sustainability for the development and diffusion of teaching materials and tools on design for sustainability

It is a shared opinion that sustainable development requires a system discontinuity, meaning that radical changes in the way we produce and consume are needed. Within this framework there is an emerging understanding that an important contribution to this change can be directly linked to decisions taken in the design phase of products, services and systems. Design schools have therefore to be able to provide design students with a broad knowledge and effective Design for Sustainability tools, in order to enable a new generation of designers in playing an active role in re-orienting our consumption and production patterns. This paper presents the intermediate results of the LeNS China, the Learning Network on Sustainability of Chinese design Higher Education Institutions aiming at curricula development on Design for Sustainability. The project is a regeneration of the LeNS Asian-European multi-polar network project financed by the European Commission. LeNS China is taking in consideration the local needs, interests and opportunities could represent a significant enabling platform capable to sensitise, support and empower a new generation of Chinese design educators, designers and entrepreneurs to reach design practice throughout an open collaborative learning approach. The paper will firstly introduce the LeNS project and its ethos, and then the LeNS China network will be described in terms of the state of the art of design for sustainability and its education in China, the scope and the objective, the results achieved so far and the next steps.

Reliability Assessment Tool Based on Deep Learning and Data Preprocessing for OSS

Recently, many open source software (OSS) developed by various OSS projects. Also, the reliability assessment methods of OSS have been proposed by several researchers. Many methods for software reliability assessment have been proposed by software reliability growth models. Moreover, our research group has been proposed the method of reliability assessment for the OSS. Many OSS use bug tracking system (BTS) to manage software faults after it released. It keeps a detailed record of the environment in terms of the faults. There are several methods of reliability assessment based on deep learning for OSS fault data in the past. On the other hand, the data registered in BTS differences depending on OSS projects. Also, some projects have the specific collection data. The BTS has the specific collection data for each project. We focus on the recorded data. Moreover, we investigate the difference between the general data and the specific one for the estimation of OSS reliability. As a result, we show that the reliability estimation results by using specific data are better than the method using general data. Then, we show the characteristics between the specified data and general one in this paper. We also develop the GUI-based software to perform these reliability analyses so that even those who are not familiar with deep learning implementations can perform reliability analyses of OSS.