Research on the Relationship Between Learning Motivation and Neural Activity in the Learning Process of Instructional Video:A NIRS Study

As the intrinsic driving force to promote learner’s learning,learning motivation is one of the key factors that affect learning engagement and efficiency.In terms of optimizing instructional videos and strengthening learning effects,it is particularly important to understand the cognitive neural mechanism and influencing factors of the changes of learning motivation.By using the near-infrared spectrometer technology,the paper has collected the state of neural activity while learners were learning different instructional videos,and has analyzed the relationship between the learning motivation of instructional videos and the state of neural activity in the learning process from the angle of cognitive neuroscience.It is found that both the intrinsic and extrinsic learning motivation of instructional videos will affect the state of neural activity in the learning process;the learning process will also affect the intensity of learning motivation,while the preparation of fine instructional videos will also cause the transfer of learning motivation.

Impacts of Online Remote Education on the Learning Process among Nursing Students

The period of existence and spread of Corona virus has led to the use of all means of remote education as an urgent necessity for all educational facilities, especially universities. Aim: Therefore, it was necessary to study the impacts of online remote education on the learning process among nursing students through studying of two courses;health information management at 6th semester and gerontology nursing course at 4th semester. Tool of Data Collection: A modified questionnaire comprised of forty statements was used through paper-based survey and online survey. Sample: A total samples (224) of nursing students were participated in the survey who enrolled in 2019 and 2020 spring semesters. Setting: The field of study was the nursing department of Applied Medical Science at Misr University for Science and Technology. Results: Induced positive impacts of online education on the learning process for nursing students experience were proven as more than half of the students (53.9%) had prior experience on online system use, and more than two thirds (62.5%) were competent in mobile/computer applications. Almost, two thirds (59.3%) agreed about online assessment experience, except that the online exam was anxious, and the time was insufficient to answer all questions. Also, more than two thirds (64.7%) agreed about the learning process of the two nursing courses. Conclusion: The study concluded that there were positive impacts of online education system on the learning process for nursing students except that the students were not able to decide that the remote online education system can replace traditional face-to-face learning as the clinical experience was not evaluated through this study. Recommendation: This study is recommended to be repeated on a large scale of participants to assess the possibility of achieving clinical experience through online remote education if Corona virus still coexists.

Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.

B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.

Some Knacks of Studying English Writing： From My Own Learning Experience

A learner's stages of L2 development are connected by his or her L1 and culture. It is, accordingly, of paramount impor-tance to understand the second language learners' culture and learning process and better assist them through this process in theway of teaching them English writing.This essay has demonstrated several elements which affect learners' English writing throughmy own experiences, such as different cultures, reading and correct recognition of writing, and the writing process.

An Optimal Method for Speech Recognition Based on Neural Network

Natural language processing technologies have become more widely available in recent years,making them more useful in everyday situations.Machine learning systems that employ accessible datasets and corporate work to serve the whole spectrum of problems addressed in computational linguistics have lately yielded a number of promising breakthroughs.These methods were particularly advantageous for regional languages,as they were provided with cut-ting-edge language processing tools as soon as the requisite corporate information was generated.The bulk of modern people are unconcerned about the importance of reading.Reading aloud,on the other hand,is an effective technique for nour-ishing feelings as well as a necessary skill in the learning process.This paper pro-posed a novel approach for speech recognition based on neural networks.The attention mechanism isfirst utilized to determine the speech accuracy andfluency assessments,with the spectrum map as the feature extraction input.To increase phoneme identification accuracy,reading precision,for example,employs a new type of deep speech.It makes use of the exportchapter tool,which provides a corpus,as well as the TensorFlow framework in the experimental setting.The experimentalfindings reveal that the suggested model can more effectively assess spoken speech accuracy and readingfluency than the old model,and its evalua-tion model’s score outcomes are more accurate.

Studying History Education of High School in Taiwan by Causal Layered Analysis

After the 21st century,high school history learning will focus on teachers promoting the twelve-year state education.In recent years,in line with the changes in the new 108-year social curriculum,supporting strategies have been proposed:such as literacy orientation,inquiry and practice,learning process archives,and the structural direction of the controversial Chinese history into East Asian history.Historical learning has indeed had a great impact on the people’s national spiritual education and the development of historical consciousness in Taiwan’s education policy.This is the reason Taiwan’s Ministry of Education strives to improve students’historical literacy and connotation application abilities.When developing a learning policy,both external and internal learning factors need to be considered.The external aspect deals with the reasons for learning:Is learning for the purpose of using or accumulating historical wisdom in daily life to learn from the past and the present,on the other hand,to test the content of the course and the degree of absorption;or is it specifically for exams or other enlightenment purposes.The internal aspect involves those most affected by the policy:students and teachers.After studying and observing high school history learning policies for decades,some alternative future visions for history learning were found in the method of reflection on future research-the conclusion is that history is interestingly revitalized,and the preferred future is thematic history.According to the famous futurology scholar Sohail Inayatuallah’s proposal:the causal layering model.It helps understand how Taiwan’s historical policies operate.And how teachers and students on the front line respond to changes and take future actions.The key is to change the future:in the process of building an alternative future,whether the internal and external mix has changed or whether you want to try new things and expand your horizons.In fact,the difficulty of teaching lies in students’cooperation and conscious learning.Therefore,in the analysis of learning through alternative futures,is it possible to distinguish between internal and external situations and methods such as:1.Internal:Is education centered on teachers?Or is it student-centered?2.External:Does the Ministry of Education prioritize testing,or encourage teachers to adopt interactive communication and integrate education into the curriculum?Therefore,what is the function and inspiration of studying high school history and life?If thematic history teaching is used:teachers can use thematic learning methods to help students focus on causal relationships,the causes of turning points,or the evolution process of the beginning and end of events.This is more advantageous for testing based on the application topic,and it is easy to test how much understanding and understanding of history?Has an activating effect.By studying history in high school,using the“CLA(Causal layered analysis)”method of future studies,you can enter the stage of worldview exploration with the goal of improving professional depth and emotional level,and use it in your own understanding and utilization of history.Based on research,some insights into the prospects and thinking of learning history in high schools are provided:1.Facing the impact of declining birthrate,Taiwan needs a macro perspective to improve its future competitiveness and look forward to a new perspective on world history,using futuristic cause-and-effect level analysis to combine world changes with daily life applications.2.The study of history in high schools should go into a systematic construction:understand its cause-and-effect relationships and global trends,so teachers play a professional and future role in controlling the use of new information and technology.3.In the future,humans may develop more“intelligent”needs.As a reference from history or to explore the preferred path for the future,there will also be a greater need to innovate and meet challenges.4.Studying high school history has entered the professional field.Through self-exploration,it can be transformed into life affairs and establish the concept and value of lifelong learning.5.In studying the“history of high school learning”,have new prospects for the future of education.Through professional knowledge such as“trend theory and causal hierarchy analysis”of futurology,pursue new horizons and visions,making future education full of hope and possibility.

Contrastive Rhetoric and Inter-language Theory

A learner’s stages of L2 development are connected by his or her L1 and culture.It is,accordingly,of paramount importance to understand the second language learners’culture and learning process and better assist them through this process in the way of teaching them English.Similarly,inter-language theory(IL)and contrastive rhetoric are affected by factors,such as learner’s L1,learning experiences,and culture.This paper talks about these two theory’s characteristics,constructs,and importance,so language instructors may better understand the L2 learning phenomena and think out better methods to help language learners improve their language skills.

The Podcast as an Innovative Urban Planning Teaching Tool: Soft and Hard Skills between Discipline and Professional Identity

The expression didactic innovation has recently assumed an implicit reference to Distance Learning.For scholars,however,it was above all the critical questioning on learning models.The article explores the use of the podcast as a stimulus for engineering students to achieve new soft skills:to learn multidisciplinary contents related to planning and to acquire professional competences in digital aspects and feedbacks attribution.Experience took place over two years:this gave the opportunity to compare the activity carried out totally in presence and totally remotely(through online lessons and reviews),to draw from them issues for discussion and future implementations.

Error Making Analysis in EFL Classroom

In the traditional education environment,the view of a good teacher is measured in the learners’results of examination.The higher the score,the less the learners’errors are made.Consequently,in English as a foreign language(EFL)classroom,faced with the requirements of good academic performance,it tends to be no escaping issue on the emphasis of right or wrong on the learners’performance since teaching goals require sensitivity to their errors.For this reason,the paper intends to probe into stu-dents’errors in the EFL learning from different perspectives.

Differences Between Children’s First Language Acquisition and Adults’Second Language Acquisition

There is an apparent contrast between children’s first language acquisition and adults’second language acquisition,which are mainly manifested in the following three aspects:age difference,difference in learning process and motivation difference.This paper will analyze these three differences in detail,and combine the analysis results to guide second language pedagogical implications according to the current situation.

Application of artificial intelligence in ophthalmology

Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, agerelated macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.

DSC based Dual-Resunet for radio frequency interference identification

Radio frequency interference(RFI)will pollute the weak astronomical signals received by radio telescopes,which in return will seriously affect the time-domain astronomical observation and research.In this paper,we use a deep learning method to identify RFI in frequency spectrum data,and propose a neural network based on Unet that combines the principles of depthwise separable convolution and residual,named DSC Based Dual-Resunet.Compared with the existing Unet network,DSC Based Dual-Resunet performs better in terms of accuracy,F1 score,and MIoU,and is also better in terms of computation cost where the model size and parameter amount are 12.5%of Unet and the amount of computation is 38%of Unet.The experimental results show that the proposed network is a high-performance and lightweight network,and it is hopeful to be applied to RFI identification of radio telescopes on a large scale.

Call for Papers Special Section on Deep Learning for Natural Language Processing

The publication of Tsinghua Science and Technology was started in 1996.Since then,it has been an international academic journal sponsored by Tsinghua University and published bimonthly.This journal aims at presenting the state-of-the-art scientific achievements in computer science and other IT fields.

Acknowledgments

Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical

The Formation Mechanism of an Enterprise’s Digital Intelligence Transformation Capability:A Case Study on“Knowledge-Action Oneness”in Haier

The rapid development of big data,artificial intelligence(AI),and blockchain technology makes the digital intelligence transformation of an enterprise possible.Based on the case study of Haier Group,this paper attempts to address the rationales behind building up the capability of digital intelligence transformation of enterprises by means of the traditional Chinese idiom,“knowledge-action oneness.”The result indicates that the learning process is an important factor for an enterprise to form its digital intelligence transformation capability.It is a process of mutual coupling between digital knowledge and digital actions.As a result of such a unity,different learning subjects form their corresponding digital intelligence transformation capability through their own learning process of mutual coupling of knowledge and action:Leaders form digital strategic capabilities through the mutual coupling of strategic knowledge and actions;employees form digital absorption capabilities through the mutual coupling of scenario-based knowledge and actions;teams form digital integration capabilities through the mutual coupling of integrated knowledge and actions;and the whole organization forms digital,eco-systemic capabilities through the mutual coupling of institutionalized knowledge and autonomous actions.Such a multi-level digital intelligence transformation system requires efforts from everyone in the enterprise.

Automated extraction of attributes from natural language attribute-based access control(ABAC)Policies

The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.

Automated extraction of attributes from natural language attribute-based access control(ABAC)Policies

The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.