Reviewerly/exHarmony huggingface dataset:
Deep transfer learning with multimodal embedding to tackle cold-start and sparsity issues in recommendation system

Recommender systems (RSs) have become increasingly vital in the modern information era and connected economy. They play a key role in business operations by generating personalized suggestions and minimizing information overload. However, the performance of traditional RSs is limited by data sparseness and cold-start issues. Though deep learning-based recommender systems (DLRSs) are very popular, they underperform when considering rating matrices with sparse entries. Despite their performance improvements, DLRSs also suffer from data sparsity, cold start, serendipity, and generalizability issues. We propose a multistage model that uses multimodal data embedding and deep transfer learning for effective and personalized product recommendations, and is designed to overcome data sparsity and cold-start issues. The proposed model includes two phases. In the first-offline-phase, a deep learning technique is implemented to learn hidden features from a large image dataset (targeting new item cold start), and a multimodal data embedding is used to produce dense user feature and item feature vectors (targeting user cold start). This phase produces three different similarity matrices that are used as inputs for the second-online-phase to generate a list of top-n relevant items for a target user. We analyzed the accuracy and effectiveness of the proposed model against the existing baseline RSs using a Brazilian E-commerce dataset. The results show that our model scored 0.5882 for MAE and 0.4011 for RMSE which is lower than baseline RSs which indicates that the model achieved an improved accuracy and was able to minimize the typical cold start and data sparseness issues during the recommendation process.
Transitioning from face-to-face classes to emergency remote learning

Abstract&#x0D; The COVID-19 pandemic forced higher education institutions to pivot their traditional face-to-face teaching to emergency remote teaching (ERT), causing a shift in delivery modalities. Recent studies have described ERT's existing experiences as an emerging transitioning methodological alternative to comply with the social distancing and safety guidelines stipulated globally. This research study merges data from two sources in a convergent parallel mixed method design. Thus, the study presents a systematization of the action plan undergone by the faculty authorities of Universidad Nacional of Costa Rica and a follow-up trace analysis of newcomer's experiences in the Bachelor major in English Teaching as a Foreign Language at the school of Literature and Language Sciences (ELCL, Spanish initials) during the first term of 2020. Results evince the students' experience in tech possibilities, electronic device ownership, and healthy, economic, social, or physical learning setting issues. Lastly, the researchers propose an empathetic design thinking further research study to determine where to integrate technology at the BA TEFL major.&#x0D; &#x0D; Keywords: COVID-19 pandemic, face-to-face teaching, emergency remote teaching, newcomer students' experiences, higher education&#x0D;
Building a cooperative teaching process in teaching physical education in high schools in Vietnam

Cooperative teaching in Physical Education plays an important role in helping students actively participate in learning movements and develop physical qualities, creating opportunities for them to share their experiences with each other and ideas to solve common problems related to the lesson.Stemming from that meaning, and on the basis of surveying the current situation of cooperative teaching, the article has built a process of cooperative teaching in PE at high schools in Vietnam in order to contribute to the organization of cooperative learning scientific teaching and improve the quality of physical education teaching in high schools.
Continuous pedagogical education: value orientations of entry into profession

Introduction. The topic of this article is relevant because there is no consistency in the formation of professional pedagogical values and orientations in the implementation of educational programs at different stages of teacher training. The purpose of the study is a comparative analysis of the value-semantic orientations of professional pedagogical activity of future teachers at the stage of training in a pedagogical college and university. Methods. The study involved students of pedagogical colleges (N=147) of the Yaroslavl region and students of the Yaroslavl State Pedagogical University named after K.D. Ushinsky (N=253). To study professional values and meanings, a questionnaire was developed that includes three lists of instrumental values focused on the child, environment and profession and a set of Must-topics that reveal the respondents' attitude to the priorities and problems of teacher training. Empirical data were processed using the methods of primary descriptive statistics, correlation, structural, and factor analysis. The results of the study. Significant differences in the value-semantic structure of the profession of future teachers were revealed: for university students, the enrichment of students' life experience is more significant than for college students (N=7,061, p&lt;0,05); university students value knowledge of technologies of diagnostic and correctional-developmental work with students (N=8,203, p&lt;0,05) and ways of developing students' self-management (N=7,749, p&lt;0.05) more. Structural analysis revealed the first place of student development values in professional activity (average values of 20,4 for college students and 15,8 for university students). Factor analysis showed a high value of knowledge values for college and university students. At the same time, college students have more factor weight of knowledge of ways to cooperate with all participants in the educational process (0,609), and university students have more factor weight of knowledge of ways to involve students in various activities (0,565). Conclusion. The conducted research has shown that students' value orientations are equal to the guidelines declared by the pedagogical community. The hypothesis about the differences in the value-semantic orientations of future teachers at various stages of professional training has been confirmed. Students of a pedagogical university have a more balanced structure of values-goals: both in the value system and in individual groups of instrumental values, the priority sequence "child-environment-profession" is visible. College and university students attach more importance to values-knowledge. It is necessary to increase practical training at all stages of teacher training.
Error Analysis in Spoken English among Grade 12 Students

This study aimed to identify and categorize the errors in spoken English among Grade 12 students based on the Linguistic Category Taxonomy of classification of errors by Dulay et al. (1982). Mixed sequential explanatory research design was used in the study. Participants were chosen from the two Senior High School strand offered at Aguinaldo T. Repiedad Sr. Integrated School using stratified random sampling. Recorded utterances from the picture-description and interview were analysed for errors using frequency count, mean rank, percentage and Mann-Whitney U Test to determine the major errors and the significant differences of these errors when participants were grouped according to academic track and exposure to English-related media. Results showed that students committed more errors in syntax and articulation of consonant sounds. Moreover, they have difficulty with noun and verb inflections in terms of its morphological component. Additionally, in terms of its syntactical component, the three highest number of errors recorded were on verb, preposition, and noun usage. On the other hand, the students’ Senior High School track/strand or exposure to English-related media have no direct influence on the extent of errors the students committed in their utterances. Based on the results, it is recommended that English instruction be strengthened especially in teaching grammar and pronunciation and use authentic and relevant assessment tools when teaching to allow students to practice oral fluency of the target language.
Study on the Improvement of College Teachers' Informationized Teaching Ability

The 13th Five-Year Plan clearly proposes to enhance teachers’ ability of informationized teaching, so as to make informationized teaching a routine mode. In 2020,in order to prevent and control COVID-19, colleges have launched online teaching activities, which is necessary to use information technology in teaching and also a test of teachers’ ability of informationized teaching. In this context,teachers should change their teaching ideology and improve their ability of informatizationized teaching in an all-round way. Firstly, this paper analyzes the current situation of university teachers’ informationized teaching. Secondly, it analyzes the significance of improving the informationized teaching ability of university teachers. Finally, it analyzes the promotion strategies of university teachers’ ability of informatization teaching .
The effectiveness of reciprocal scaffolding technique on students’ writing skill

The objectives of this research were to investigate the effectiveness of using the reciprocal scaffolding technique on students’ writing skills and the students’ responses toward the implementation of the technique in the teaching and learning process at the second-year student of State Junior High School 1 Kutamakmur, North Aceh. The samples for this study were drawn in two customized groups using a random sampling approach with class VIII/b as the experimental group and class VIII/d as the control group forming a total of 52 students. The data for this study were gathered using a test and a questionnaire. The data were analyzed to determine the difference in the t-test scores between the two groups. The statistical results showed that the control group’s mean pre-test score was 50.15, which was lower than the experimental group’s 51.69. Meanwhile, the mean score of the experimental group in the post-test was 70.88, which was higher than the control group’s 58.34. The significant value for both groups in the post-test was 0.00, which is less than 0.05. Therefore, the null hypothesis was rejected and the alternative hypothesis was accepted. It indicates that teaching writing while employing reciprocal scaffolding more effectively improves the students’ writing than teaching without it. In addition, the questionnaire that was presented to the experimental group revealed that the students gave positive responses toward the use of reciprocal scaffolding techniques in teaching writing.
The User Experience Design Program: Applying Situated and Embodied Cognition Together With Reflective Teaching

The education of students to become competent user experience designers is a delicate matter as students need to obtain a multitude of knowledge, skills, and judgmental abilities. In this paper, our effort to manage this multiplicity in a bachelor's program in user experience design is shared along with our experiences and teaching practices influenced by theories of situated and embodied cognition together with reflective teaching. The program was followed up through interviews with eight alumni and a company representative that employs user experience designers. The results show that the program overall works well, although some of the identified issues need to be addressed in the future. The interpretation is that our program curricula and teaching practices are fruitful, which hopefully can contribute to thoughts and discussions for other teachers in the field of user experience design and human-computer interaction.
Specific Relation Attention-Guided Graph Neural Networks for Joint Entity and Relation Extraction in Chinese EMR

Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction.
Measurement of Technology Acceptance Model (TAM) in Using E-Learning in Higher Education

Information and Communication Technology (ICT) growth triggers various educational institutions, especially universities, to use technology in their learning. The learning model with ICT is known as e-learning which is designed using a Learning Management System (LMS). Ahmad Dahlan University (UAD) has been using e-learning since 2009. Currently, an e-learning system is very much needed in the online learning process. The UAD e-learning development team developed a new e-learning system to overcome distance education during the pandemic. This study was carried out to measure TAM in the use of UAD's new e-learning. The factors used are based on previous research using two primary factors from TAM, namely perceived usefulness and perceived ease of use by adding other aspects (1) instructor characteristic, (2) habit, (3) computer self-efficacy, and (4) system quality. The number of samples was determined using the slovin method with the results of 86 people, and the technique of deciding respondents was using purposive sampling. The data processing method in this study was carried out using the SPSS 20 application. The analysis results showed that habit, computer self-efficacy, system quality, and perceived ease of use significantly correlated with perceived usefulness. In contrast, instructor characteristics had no significant relationship with perceived usefulness. The immense contribution of the influence of instructor characteristic and habit factors on perceived usefulness simultaneously is 45.8%, for computer self-efficacy and system quality factors on perceived ease of use are 41.3% and for perceived ease of use factors on perceived usefulness is other variables influence 33, 2% and the rest.
On Teaching of English Majors’ Thesis Writing Based On Production-Oriented Approach

With the deepening of higher education reform, how to considerably improve the quality of English major students in universities from the perspective of thesis writing has become indispensable for educators.Comparing the teaching method of traditional writing course, this article explores the feasibility of the Production-oriented Approach (POA) in teaching thesis writing of English majors.In addition, from the three elements of POAmotivating, enabling and assessing, this paper put forwards viable teaching suggestions based on POA for thesis writing of English majors.Significantly, this article aims to cultivate students' consciousness of innovation and proficiency of scientific practice, thus enhancing the academic literacy of English majors.
Penggunaan Aplikasi Google Classroom Sebagai Metode Pembelajaran Daring Selama Pandemi Covid-19

Covid-19 has now attacked Indonesia, where the spread of the disease is very fast. Not only in Indonesia, but all corners of the world are currently experiencing a health crisis. In the beginning, the spread of Covid-19 had an impact on economic activity which began to sluggish. This also has an impact on the education system in Indonesia. Until several countries decided to close schools and universities. In an effort to prevent the spread of covid-19, the World Health Organization (WHO) recommends temporarily stopping activities that would potentially cause crowds. Even during the outbreak, covid-19 in Indonesia, there were many ways that the government did to prevent its spread through social distancing. Kemendikbud instructed through the Ministry of Education and Culture (Kemendikbud) Directorate of Higher Education Circular No. 1 of 2020 concerning the prevention of the spread of covid-19 in the world of Education to organize distance learning and advise students to learn from their homes. Teachers and students are starting to be required to follow the current situation by using technology as a distance learning medium. One of the media that is being favored by teachers as a learning medium is the Google Classroom application. This application is an application that can make it easier for students and teachers to create effective learning. Given that students today are a generation who are very familiar with the use of technology. The use of technology in learning is an alternative method used by teachers during the Covid-19 Pandemic.
Aliasing and adversarial robust generalization of CNNs

Abstract Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. To reveal model weaknesses, adversarial attacks are specifically optimized to generate small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained by using adversarial examples during training, which in most cases reduces the measurable model attackability. Unfortunately, this technique can lead to robust overfitting, which results in non-robust models. In this paper, we analyze adversarially trained, robust models in the context of a specific network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from downsampling artifacts, aka. aliasing, than baseline models. In the case of robust overfitting, we observe a strong increase in aliasing and propose a novel early stopping approach based on the measurement of aliasing.
A survey on solving dilemmas of adapting blockchain in different applications

This work is going to address the dilemmas and challenges that researchers face when selecting blockchain to solve a problem in a particular domain. Since blockchain is an emerging technology and it is intended to solve many security issues in various domains, People have confusion when to use blockchain and when not to use it. The aim is to investigate which are the different applications where blockchain technology can be a solution and also it aims to discuss which type of applications does not need blockchain to be incorporated. The main contribution of this paper is Survey on blockchain technology analyzing when to use blockchain technology, Survey on when need not use blockchain technology, some research problems where blockchain can be implemented and a final discussion about blockchain adaption.
Learning term weights by overfitting pairwise ranking loss

A search engine strikes a balance between effectiveness and efficiency to retrieve the best documents in a scalable way.Recent deep learning-based ranker methods are proving to be effective and improving the state-of-the-art in relevancy metrics.However, as opposed to index-based retrieval methods, neural rankers like bidirectional encoder representations from transformers (BERT) do not scale to large datasets.In this article, we propose a query term weighting method that can be used with a standard inverted index without modifying it.Query term weights are learned using relevant and irrelevant document pairs for each query, using a pairwise ranking loss.The learned weights prove to be more effective than term recall which is a probabilistic relevance feedback, previously used for the task.We further show that these weights can be predicted with a BERT regression model and improve the performance of both a BM25 based index and an index already optimized with a term weighting function.
Performance Evaluation for College Curriculum Teaching Reform Using Artificial Neural Network

To address the problems of poor performance evaluation and performance management of college curriculum reform, the performance evaluation method of college curriculum reform using artificial neural networks is proposed. First, the performance evaluation index system of college curriculum reform using artificial neural network technology is constructed. Second, the performance evaluation algorithm of college curriculum reform is improved, and the performance evaluation process of college curriculum reform is simplified. The experiment proves that the performance evaluation method of college curriculum reform using artificial neural networks has higher practicality than the traditional method and fully meets the research requirements.
Research on Video Object Detection Methods Based on YOLO with Motion Features

Aiming at the fixed-view video surveillance scene, this paper proposes a video object detection method that combines motion features and YOLO. The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model. In this process, video frames containing moving objects are first obtained by the moving object detection module. Second, the moving target will be recognized by the object of interest recognition module. Finally, the background decision module records and analyzes the detection results to obtain background model updates or result output. It detects moving objects without using traditional background modeling methods. Experiments based on the CDnet2014 dataset show that our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62% compared with the YOLO-based humanoid detection method. Furthermore, the method has superior performance in scenarios where target objects appear less frequently (substations, transmission lines, and hazardous areas).