A Hybrid Virtual Cloud Learning Model during the COVID-19 Pandemic

The COVID-19 pandemic has affected the educational systems worldwide,leading to the near-total closures of schools,universities,and colleges.Universities need to adapt to changes to face this crisis without negatively affecting students’performance.Accordingly,the purpose of this study is to identify and help solve to critical challenges and factors that influence the e-learning system for Computer Maintenance courses during the COVID-19 pandemic.The paper examines the effect of a hybrid modeling approach that uses Cloud Computing Services(CCS)and Virtual Reality(VR)in a Virtual Cloud Learning Environment(VCLE)system.The VCLE system provides students with various utilities and educational services such as presentation slides/text,data sharing,assignments,quizzes/tests,and chatrooms.In addition,learning through VR enables the students to simulate physical presence,and they respond well to VR environments that are closer to reality as they feel that they are an integral part of the environment.Also,the research presents a rubric assessment that the students can use to reflect on the skills they used during the course.The research findings offer useful suggestions for enabling students to become acquainted with the proposed system’s usage,especially during theCOVID-19 pandemic,and for improving student achievementmore than the traditional methods of learning.

Research on Talent Cultivation Model and Application Based on Mobile Cloud Learning Technology-Taking Innovative Talent Cultivation as an Example

Mobile cloud learning and innovation and entrepreneurship education are undoubtedly one of the hot issues in the current development of human resources and education. At present, domestic colleges and universities still lack the industrialization model and platform that apply theory to practice areas, and apply the new mobile cloud learning technology to the research and practice of innovation and entrepreneurship training.

S^(2)ANet:Combining local spectral and spatial point grouping for point cloud processing

Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.

A Novel Hidden Danger Prediction Method in CloudBased Intelligent Industrial Production Management Using Timeliness Managing Extreme Learning Machine

To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.

In the Future, Learning in the Cloud Will be More Effective than Learning in A Physical University

With the progress of technology,learning in the cloud has becoming the new teaching and learning model in education.Compared with the traditional class,it influences on improving academic performance,stimulating learning motivation and saving time.However,most changeling of it is increased of dropout.What is more,the feeling of lacking interpersonal communication happened between the teachers and students.

IoTFLiP： IoT-based flipped learning platform for medical education

Case-Based Learning （CBL） has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things （IoTs） concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical eases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learning environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastrneture is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool （ICBFLT）, which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students＇ and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students＇ academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.

Prioritization technique for learning cloud services

Purpose–The purpose of this paper is to propose a combined technique of cumulative voting and numerical assignment to prioritize the services of the learning cloud service.Design/methodology/approach–The approach starts with requirement elicitation,then analyses of the requirements in terms of prioritization and finally classifies the priority of services into groups.Findings–As a result of the case study the requirements of the College of Art Media and Technology students has been classified into three service groups.Originality/value–This combined prioritized techniques can involve learners in the decision making process about learning cloud services utilization in the organizations.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.