Reviewerly/exHarmony huggingface dataset:
Exploration and Application of Western Blotting in Comprehensive Experiment Teaching of Biochemistry

Cultivating high-quality medical talent with the ability to adapt to the rapid development of modern medicine and the capability of independent innovation is the core of medical education in China. Biochemistry experiment teaching is an integral part of the basic medical sciences. How to set up a bridge in biochemical experiment teaching to connect the knowledge of biochemical theory and cultivate students' independent innovation ability is a focus of universal concern. In this article, we have redesigned an original comprehensive experiment to detect the protein expression level of IL-1β by western blotting in livers of rats with acute liver failure (ALF) induced by LPS/D-galactosamine (D-GalN). Students are involved in the entire experimental process of western blotting, including the construction the rat model of acute liver failure, extraction and quantification of liver total proteins and the experiment and result analysis of western blotting. All operations during the experiment are completed by students independently under the instructor guidance. The grade of biochemistry experiment course is evaluated according to the reformed standard. The western blotting comprehensive experiment for medical undergraduates not only stimulate students' interest and initiatives, increases students' basic experimental skills and hands-on abilities, but also improves students' comprehensive ability to analyze and solve problems, and enhances students' innovation capability.Therefore, the western blotting comprehensive experiment teaching is an effective teaching method to enhance the comprehensive quality of medical students, and is feasible to carry out among medical undergraduates.
What Kind of Training Program Would You Prefer: An Empirical Study on Language Assessment Literacy of Rural Teachers

Recent years, "assessment for learning" is replacing "assessment of learning" as one of the most important aims of assessment, which requires language teachers to develop higher language assessment literacy (LAL). The foundation of developing an effective training programme is to diagnose the current level of teachers' LAL and to identify their training preferences. The present study reflects that rural teachers' weaknesses in LAL mainly fall on reporting assessment results to stakeholders, developing effective scoring criteria and using assessment results for decision-making. Meanwhile, it is found that teachers hold high expectations for participating in the training program, and their typical assessment contexts are investigated to develop targeted training themes. Finally, measures to motivate teachers are proposed based on interview.
A Study on the Effectiveness of Podcasting as An Instructional Intervention for Efficient Transactions in Computer Science Teaching

Podcasting in India is at a very nascent stage, despite the fact that the nation has a large number of active bloggers. In India it's growing very fast. For this purpose, researcher planned to study the effective teaching-learning techniques of podcasts for computer science in senior secondary classrooms. Purpose: This study accounts for the effectiveness of podcasts in teaching computer science in the extent of retention capability, interest of students and academic achievements of students at secondary school level. This study had shaded light on the use of technology i.e. Podcast in teaching-learning at senior secondary stage with respect to Computer Science. Design: For this study, researcher had developed podcasts with the content about two different topics of the computer science book of NCERT and used them as a tool through podcasting technique for a few weeks. This study lies in quasi-experimental design in nature. The sample for this study was taken through Random Sampling. The School was selected randomly from south-west Delhi zone. The sample size for students was 60 students from two different schools which assimilated using few parameters of class XI. In this study, 30 students were taught through podcasting mode with the help of podcasts and next 30 students through a regular lecture method. Findings: The result of the study implies that using podcasts stipulates qualitative reimbursement for students over and above regular mode (Lecture method) of teaching computer science. While the study is limited by small sample size and by some within-group variation in pedagogical techniques, the study provides initial evidence that podcasts have the potential to provide greater advantages if it is used in teaching-learning practices more than simply as a supplementary tool. Findings of this study have provided information about further research studies which includes different aspects of learning, subjects, teaching resources and learner. According to researcher, this study has shown a different outlook to the teachers and students respectively so that they can replace their traditional lecture methods, learning styles and include podcasting in education. Research limitations: This study focused solely on the schools of the south-west zone of New Delhi. Thus it would have been better if it could have covered several schools all over New Delhi and India.The study was confined to the students of the senior secondary stage only having Computer Science as an optional subject in their course.
Design of Flute Music Remote Teaching System Based on Multi-Pass Scheduling Optimization

With the gradual development of the Internet industry, every aspect of people’s life has been affected by the Internet, playing increasingly irreplaceable functions in people’s entertainment, office, and other aspects. Judging from the current development situation, the old Internet digital teaching system has many problems, such as low artificial intelligence, weak information processing ability, and lack of effective learning ability. This paper designs the flute music remote teaching system, which can realize remote music teaching and provide help in providing real-time music teaching. The music learning system includes the user’s access records, the user’s operation and the completion of the test data, the discussion and communication of online participation, the user’s interests, specialties and operation methods, learning progress and scoring, and so on. In addition, it explores and explains all the key steps required by the current distance education model and invents a sample of the distance education model. On this basis, Internet algorithm programs will be used for all key processing functions of the system. The use of Internet algorithm programs is interactive and automated, which greatly enhances the role of the education system. This article first discusses the unique teaching and automated teaching mode of the system, which lays the cornerstone for further reforms in this field in the future.
The Application of Production-oriented Approach Assessment in Senior High School English Reading Teaching

Some scholars have applied POA to high school English classes and used comparative experimental data to demonstrate the effectiveness of this approach, but few have analyzed and evaluated its application of assessment in high school English reading classes in detail. The research adopted qualitative study and action research. The study found that in the immediate assessment, understanding and analyzing questions and critical feedback were more frequent. Creating questions and process-focused feedback were limited. In the delayed assessment, there was a high degree of students' participation, and multiple assessments of group discussion, teachers' demonstration of revision, peer assessment enabled students to deepen their understanding of the assessment criteria and improve their output. Based on the results of the study, this paper provides suggestions on the application of the POA assessment to high school English reading teaching: in the immediate assessment, the number of high-cognitive questions (evaluating and creating questions), the scaffolded feedback in task-focused feedback and process-focused feedback should be increased. In the delayed assessment, guided marking can be implemented. The time for group discussion and teacher's demonstration of revision can be appropriately increased, and teachers should not be absent in the post-class peer assessment session, who are obliged to provide timely scaffolding for the students.
Perspectives in Formulating Policies to Promote Lifelong Learning in the System of Cultural Institutions Towards Building A Learning Society to Meet the Requirements of Digital Transformation in Vietnam

Lifelong learning towards creating a learning society is one of the contemporary issues in the policy and management of culture and education. This is considered as an important key for citizens of each country to survive and develop in a constantly changing world. As a result of this pressing need, each country must develop policies to promote lifelong learning through different models and methods, one of which is lifelong learning in the cultural institutional system. This special model has a dual effect: promoting the building of culture and people while also promoting education towards sustainable development.&#x0D; By analyzing, synthesizing and generalizing the current state of Vietnam's policies as researched in the context of digital transformation, the study points out new trends in policies to promote lifelong learning activities in Vietnam. The system of cultural institutions aims to build a learning society in the contemporary trend based on the philosophy: to create an open, unified, synchronous, connected system of cultural institutions on a digital transformation platform.&#x0D; Keywords: Lifelong learning, cultural institutions, digital transformation cultural and educational policy.&#x0D; &#x0D;
Policies for Promoting the Digital Transformation in Education in Ethnic Minority and Mountainous Areas

Digital transformation is an important condition to enhance opportunities for expanding and improving the quality of education in ethnic minority areas. The essence of digital transformation in education is the transformation of content, curricula, and education management based on technology for high socio-economic efficiency at low cost. Digital transformation in education opens up opportunities and contributes to social development in ethnic minority areas. However, this process faces many challenges due to the typically difficult natural and socio-economic conditions of the ethnic minority areas. Therefore, an adequate understanding of digital transformation, as well as accurate assessments of the context in order to achieve sound digital transformation policies of improving the quality and effectiveness of education, are critical for the development of ethnic minority areas.&#x0D; Keywords: policies, digital transformation, education, ethnic minorities
Research Status of Risk Cognition and Decision Making

This paper studies the Behavioral studies on the Middle East on culture, risk perception and decision-making.Cultural risk is evaluated as more essential in the business environment than political, budgetary, and financial danger.The most basic danger components are not adequately incorporated into the procedure of nation's danger measures, which are frequently utilized as a hotspot for country-specific risk data.
Education Evaluation Management Based on Blockchain Technology

In view of the problems related to the current education evaluation process, such as single evaluation index, unremarkable subject evaluation, inadequate implementation of education management measures, and low recognition of the results of education evaluation, particularly in China, more research is needed to ensure appropriate educational management. Security of the evaluation metrics and communication among institutes is very essential to ensure the education quality. In this paper, we adopt the blockchain technology to evaluate the current education. All-round evaluation of quality makes the whole process of education evaluation transparent and open and solves the defects existing in the process of education evaluation. The blockchain technology establishes a management mechanism for educational evaluation by cooperating with educational institutions and promotes the implementation of quality measures, as well as the implementation of policies. Furthermore, this provides a blockchain information data platform for educational evaluation and prevents the information from being tampered with, either in decentralized or centralized systems, thereby promoting the development of education along with quality. The attained results show that the proposed system can reduce the burden of information on the storage system significantly when compared to other traditional evaluation systems.
Study of the Reform of College Mathematics Blended Teaching Supported by Intelligent Technology

The rapid development of artificial intelligence, “Internet +,” big data, 5G, and other technologies in the twenty-first century has not only brought great changes to the field of education but also brought unprecedented opportunities and challenges to the reform and innovation of mathematics teaching. The use of intelligent technology to carry out the reform of college mathematics teaching in depth and effectively improve the quality of teaching has become a hot topic discussed by the majority of mathematics teachers. This paper carefully analyzes and sorts out the problems existing in the current intelligent college mathematics teaching and systematically studies the related theories and design principles of the blended teaching mode. The ideas and approaches of the reform of college mathematics blended teaching supported by intelligent technology are deeply discussed from the aspects of improving the level of teachers, setting up a blended teaching team supported by intelligent technology, optimizing the informatization construction of teaching environment and establishing rich teaching resources, building an online and offline classroom teaching system, personalized learning under the background of microclass and cloud class, cross-university blended learning in the network environment, and mathematics precision teaching evaluation and optimization using big data.
An Empirical Study on the Influence of Demographic Variables on Test Anxiety and Academic Performance of Middle School Students

The major objective of this research study was to explore the gender, age and type of school impacts between Exam anxiety and academic achievement of students at the secondary grade level students. A sample of 117 secondary grade students those studying IX X, XI standard students those got, 50% above academic achieving students and 125 studentsthose got 50% below academic achieving and their exam anxiety test scores , was purposefully and randomly selected from government and private school in khurda road,orissa. Data were collected by using the Test Anxiety Inventory (TAI) developed by nist and Diehl's(1990. Pearson correlation, independent sample t-test statistics and ANOVA were run for data analysis. It was found that non-significant exists between boys and girls and private and government school, different age groups of 14,15 and 16 years. There was significant negative correlation between exam anxiety and academic achievement. Results showed that no gender differences and age groups associated with exam anxiety and academic achievement Therefore, it is concluded that test anxiety is one of the factors which are responsible for students' underachievement and low performance. Thereis no such difference among boys and girls and also mode of school and age groups. However, when exam anxiety increases academic performance decrease; low exam anxiety negatively correlated with academic achievement with good grades.
Factors contributing to repository success in recruiting data deposits

What factors make data repositories successful in recruiting research data deposits from scholars? While quite a few studies outline researchers’ data management needs and how repositories can meet those needs, few have assessed the success of various approaches. This study examines infrastructure for accepting data into repositories and identifies factors influential in recruiting data deposits.
Network Data Center Traffic Predictive Model Analysis Based on Machine Learning

Due to the transient nature and uncertainty of traffic produced by applications and services, data center networks have a lot of challenges. As a response, networking as a domain is continually evolving to maintain the exponential growth in network traffic. The primary objective of this paper is to predict the network traffic before it impacts the system's performance. This paper first describes existing Machine Learning (ML) applications in telecommunications and then lists the most prominent difficulties and probable remedies for implementing them. We tried to implement different ML algorithms to predict the network traffic like Gradient Boosting (GB), Random Forest (RF), K-Nearest Neighbor (KNN), Adaptive Boosting (AB), Neural Network (NN), Decision Tree (DT), and Support Vector Machines (SVM) with different sub-parameters for predicting network traffic. Relying on a sequential dataset, we create the corresponding ML environment and present a comparison table of Mean square error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) for each model. The simulation results show that the AB and GB are the best-fitted models with performance matrix parameters like MSE 0.000 and RMSE 0.002 and 0.011, respectively. The orange tool is used to stimulate the predictive models.
Transfer Learning Technique with EfficientNet for Facial Expression Recognition System

Facial Expression Recognition (FER) systems are helpful in a wide range of industries, including healthcare, social marketing, targeted advertising, the music industry, school counseling systems, and detection in the police sector.In this research, using Deep Convolutional Neural Networks (DCNN) architecture, specifically from the EfficientNet family (EfficientNet-B0, Efficient-Net-B01, EfficientNet-B02, EfficientNet-B03, EfficientNet-B04, EfficientNet-B05, EfficientNet-B06, and EfficientNet-B07) has previously gone through a combined scaling process of combined dept, width and resolution.First, the previously frozen sublayer EfficientNet model was used for feature extraction.Next, the layer closer to the output layer is melted by several layers to be retrained in order to recognize the pattern of the CK+ and JAFFE data sets.This process is called the transfer learning technique.This technique is very powerful for working on relatively small data sets, namely CK+ and JAFFE.The main of this research is to improve the accuracy and performance of facial expression recognition models with a transfer learning approach using EfficientNet pre-trained with fine-tuning strategy.Our proposed method, specifically using the EfficientNet-B0 architecture, achieves superior performance for each of the CK+ and JAFFE datasets, achieving 99.57% and 100% accuracy in the test set respectively.
Deep Learning of Latent Edge Types from Relational Data

Many relational datasets, including relational databases, feature links of different types (e.g., actors act in movies, users rate movies), known as multi-relational, heterogeneous, or multi-layer graphs.Edge types/graph layers are often incompletely labeled.For example, IMDb lists Tom Cruise as a cast member of Mission Impossible, but not as its star.Inferring latent layers is useful for relational prediction tasks (e.g.predict Tom Cruise's salary or his presence in other movies).This paper describes a Latent Layer Generative Framework -LLGF that extends graph encoder-decoder architectures to include latent layers.The decoder treats the observed edge type signal as a linear combination of latent layers.The encoder infers parallel node representations, one for each latent layer.We evaluate our proposed framework on six benchmark graph learning datasets.Qualitative evidence indicates that LLGF recovers ground truth layers well.For link prediction as a downstream task, we find that extending Variational Graph Auto-Encoders with LLGF increases link prediction accuracy compared to state-of-theart graph Variational Auto-Encoders (up to 6% AUC depending on the dataset).
HSC-MET: Heterogeneous signcryption scheme supporting multi-ciphertext equality test for Internet of Drones

Internet of Drones (IoD) is considered as a network and management architecture, which can enable unmanned aerial vehicles (UAVs) to collect data in controlled areas and conduct access control for UAVs. However, the current cloud-assisted IoD scheme cannot efficiently achieve secure communication between heterogeneous cryptosystems, and does not support multi-ciphertext equality tests. To improve the security and performance of traditional schemes, we propose a heterogeneous signcryption scheme (HSC-MET) that supports multi-ciphertext equality test. In this paper, we use a multi-ciphertext equality test technique to achieve multi-user simultaneous retrieval of multiple ciphertexts safely and efficiently. In addition, we adopt heterogeneous signcryption technology to realize secure data communication from public key infrastructure (PKI) to certificateless cryptography (CLC). At the same time, the proposed scheme based on the computation without bilinear pairing, which greatly reduces the computational cost. According to the security and performance analysis, under the random oracle model (ROM), the confidentiality, unforgeability and number security of HSC-MET are proved based on the computational Diffie-Hellman (CDH) problem.
KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity.