Reviewerly/exHarmony huggingface dataset:
A Semi-Supervised Relation Extraction Method: Combining TSP Rules and Active Learning

Relationship extraction is an important step in the process of building a knowledge graph. Sentence-level relation extraction is often purely based on rules or deep learning models, which have certain defects in effect. Rule-based methods rely on the quality of manually defined rules. The deep learning method needs a lot of labeled data as a training set, however, labeled data needs time and cost. Therefore, we propose a relationship extraction method combining rules and deep learning. Its main idea is to use a small amount of seed data to automatically mine rules to label a batch of data, and then use the data labeled by rules to train the deep learning model. And we propose to adopt active learning to improve the fitting ability of the model. The experiment proves the effectiveness of our method.
Research on Dual-channel Text Feature Extraction Method Based on Neural Network

In the existing research on Chinese natural language processing text feature extraction has always been a core problem in this field, and the advantages and disadvantages of text feature extraction performance are important factors that directly affect the performance of text processing technology. This research offers a dual-channel feature extraction method based on neural networks in order to handle this challenge of text feature extraction from the perspectives of local information and context global information of text sentences. The text's local features are extracted using GCN, while the text's overall semantic features are extracted using BiLSTM, and the full connected neural network is used to fuse the local eigenvector with the global eigenvector to obtain the final features of the text. To confirm the efficiency of the suggested feature extraction technique, the suggested approach is used to classify news material in this research. The outcomes of the experiments demonstrate that the performance of the suggested method outperforms that of the GCN and BiLSTM feature extraction methods used independently. When compared to other feature extraction approaches, this method's experimental results are still better.
Design of Substation Intelligent Inspection Scheme Based on Multi-Dimensional Inspection Robot System

The digital transformation of substation is imminent. According to the business situation in the actual operation of substation, this paper analyzes the pain points of the existing substation operation inspection work, puts forward the overall framework of substation intelligent inspection based on the multi-dimensional inspection robot system of substation, and constructs the technical framework and implementation ways of each sub business module according to various business needs. Based on the data sharing and intelligent algorithm of the multi-dimensional inspection robot system, the functions of each sub business module are integrated to build a complete substation intelligent inspection solution, which provides a new idea for the intelligent upgrading of substations.
Research on Capturing Random Burst Signals of Power Satellite Internet of Things

Aiming at the random access technology which has great application prospect in power satellite Internet of Things, the random burst signal acquisition method is studied. The key problem of random burst signal acquisition is the detection of the existence of random burst signal. Firstly, the existence detection problem of random burst signals with precursors is modeled as a composite hypothesis testing problem. The signal-to-noise ratio (SNR) estimation method in the detection problem is analyzed quantitatively, and the calculation method of the theoretical upper bound of the detection performance is analyzed. Then, based on the generalized likelihood ratio (GLRT) test theory, a constant false alarm rate (CFAR) detector is designed, and the engineering implementation method of the detector is derived. Finally, four simulation experiments prove that the method has good detection performance and adaptability to received signal power fluctuations.
Application of Apriori Algorithm in the Development of Hotel Management Information

The Apriori algorithm can effectively improve the efficiency and quality of the same time help accelerate the construction of smart hotels. Starting from understanding the application advantages of Apriori technology in the teaching of hotel management in higher vocational colleges, combining teaching cases to explore its specific application methods, and committed to giving It is hoped that the reports and related data generated by the evaluation system can be freely used by users, and users can operate and calculate the obtained data by themselves. higher vocational colleges. The thesis mainly discusses the information development and construction of the hotel management major courses in universities, taking the information development and construction of the marketing courses in this major as an example to carry out innovative applications of teaching reforms.
The Impact of Using Educational Applications in Academic Achievement for Nursing Students in Jeddah City

There is a major effect of using educational apps on learning performancefor nursing students. Educational apps help students by providing different and effective education tools which can increase student success in their subjects. On the other hand, technology assists students in managing their time through apps, whichcan refine the quality of the student's life. In this study, we want to measure the effect of educational apps on nursing students. Previous research is proving there is a relationship between educational apps and nursing students' learningperformance.Technology is providing techniques that help many students in the health field. Many researchers conclude that apps have a positive effect on nursing students' skills and knowledge. This descriptive study uses an online survey sent to nursing students at different levels and universities in Jeddah city to test the changes in their academic achievement after using educational apps for nursing subjects. Nursing students from different universities in Jeddah city (Jeddah University, King Abdul-Aziz University, and King Saud University) responded positively to our survey, and the result shows most nursing students in 2nd year started using the apps and tablets. GPA (grade point average) has significantly improved for students who use a tablet.
Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus

Low-resource text-to-speech synthesis is a very promising research direction. Mongolian is the official language of the Inner Mongolia Autonomous Region and is spoken by more than 10 million people worldwide. Mongolian, as a representative low-resource language, has a relative lack of open-source datasets for its TTS. Therefore, we make public an open-source multi-speaker Mongolian TTS dataset, named MnTTS2, for related researchers. In this work, we invited three Mongolian announcers to record topic-rich speeches. Each announcer recorded 10 h of Mongolian speech, and the whole dataset was 30 h in total. In addition, we built two baseline systems based on state-of-the-art neural architectures, including a multi-speaker Fastspeech 2 model with HiFi-GAN vocoder and a full end-to-end VITS model for multi-speakers. On the system of FastSpeech2+HiFi-GAN, the three speakers scored 4.0 or higher on both naturalness evaluation and speaker similarity. In addition, the three speakers achieved scores of 4.5 or higher on the VITS model for naturalness evaluation and speaker similarity scores. The experimental results show that the published MnTTS2 dataset can be used to build robust Mongolian multi-speaker TTS models.
EXPERIMENTAL TEACHING IN MICROWAVE ELECTRONICS: THE USE OF A ROBOT FOR PROTOTYPING

Appears in: INTED2023 Proceedings Publication year: 2023Page: 6802 (abstract only)ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1834Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
INTRODUCTION OF SELF-PACED METHODOLOGIES IN BASIC ELECTRONICS LABORATORIES

E.G. Marin A. Toral-LopezJ. Cuesta-LopezA. Medina-RullF. RuizA. GodoyJ. BanqueriS. CuadrosM.C. FernandezF. Pasadas
COMPUTER TOOL TO GENERATE RAINFALL DATA FOR EDUCATIONAL PURPOSES IN SPAIN (PLUGEN)

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 5315-5321ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1379Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
CHALLENGES AND BENEFITS OF TEACHING COMMUNITY INTERPRETING TO STUDENTS AT UNIVERSITY

1 Kazan Federal University (RUSSIAN FEDERATION)2 Kazan Innovative University named after V.G. Timiryasov (IEML) (RUSSIAN FEDERATION)
PROPOSALS FOR THE IMPLEMENTATION OF AN AUTOMATED SYSTEM FOR ONTOLOGIES COMPARING

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 7465-7473ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.2043Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Analysis of New Education Technology method as Innovation and Entrepreneurship System in the Teaching-learning Process at the University of Tijuana, B.C., Mexico

A relevant investigation was made to evaluate the methods used in a high school of the Tijuana city, located in the northwest of the Mexican Republic and is a border city, having a commercial and educative treatments with the California State of the United States of America (USA), sending and receiving students to increase the scholar yielding. This is important because can have more specialized knowledge and skills to when finish his professional studies of the university and go to works in industrial, commercial, health and other type of jobs, can solve fast and efficient complicated situations that sometimes persons with a lot experience can’t solve because need this specialized knowledge. In this scientific study made in 2022, was analyzed some tools of industry 4.0 as internet of things (IOT) to capacitate professors of some topics of mathematics topic using this innovation and entrepreneurship system to increase the capacity of teachers and with this the skills of students of a public university in this important city of Mexico
DETERMINING INFLUENCE OF UNIVERSITY’S ORGANIZATIONAL CULTURE ON THE HIGHER EDUCATION QUALITY

The article describes the conceptual principles of forming the organizational culture of a higher education institution. The conducted research showed the existence of a correlation between the organizational culture of the higher education institution and the quality of its educational services. The concept of organizational culture is interpreted as a set of common values, norms, principles, rules, traditions that unite university employees and its students into a single integrated system, affecting their behavior both in the educational institution and outside of it. There is no universal model of the organizational culture of a higher education institution; specificity of activity, management style, approach to communication with stakeholders etc. – all this causes differences in organizational cultures of different universities. The process of formation of organizational culture is long and dynamic; it is influenced by a complex of exogenous and endogenous factors. The organizational culture allows the participants of the educational process to gain a sense of identity and belonging to a system; it strengthens the trust and deepens the interaction between teachers and students; it develops the set of incentives for innovation, creativity, entrepreneurship and constant improvement of the quality of educational services. The author emphasizes that the organizational culture of a higher education institution motivates teachers and students to take responsibility for the results of their activities, to improve the quality of teaching and learning. The increase in the quality of educational services has a positive effect on the competitiveness of university graduates in the labor market, on the level of satisfaction of employers’ requests for qualified and competent personnel, as well as on the innovative activity of business and the pace of innovative development of the national economy.
Binary Whale Optimization Algorithm with Logarithmic Decreasing Time-Varying Modified Sigmoid Transfer Function for Descriptor Selection Problem

In cheminformatics, choosing the right descriptors is a crucial step in improving predictive models, particularly those that use machine learning algorithms. Recently, researchers in cheminformatics have been lured to swarm intelligence to optimize the process of discovering relevant descriptors in the wrapper feature selection. This work introduced a new Binary Whale Optimization Algorithm, which utilized a novel time-varying modified Sigmoid transfer function with a modified logarithmic decreasing time-varying update strategy to improve the balancing of exploration and exploitation in WOA. The new Binary Whale Optimization Algorithm is integrated with wrapper feature selection and validated on descriptor selection problem to improve Amphetamine-type stimulants drug classification result. The suggested approach is compared to well-known swarm intelligence algorithms, and the results demonstrate its superiority.
Machine learning models applied in analyzing breast cancer classification accuracy

There have been many attempts made to classify breast cancer data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. In this study machine learning (ML) models are reviewed and compared. This paper presents the classification of breast cancer data using various ML models. The effectiveness of models comparatively evaluated through result using benchmark of accuracy which was not done earlier. The models considered for the study are k-nearest neighbor (kNN), decision tree classifier, support vector machine (SVM), random forest (RF), SVM kernels, logistic regression, Naïve Bayes. These classifiers were tested, analyzed and compared with each other. The classifier, decision tree, gets the highest accuracy i.e. 97.08% among all these models is termed as the best ML algorithm for the breast cancer data set.
Application of Blockchain Technology in Logistics and Supplychain Management

Abstract: The technology which can be called as a distributedledger that records transactions between two parties is Blockchain. When we look into the current problems of threats pertaining to security and privacy in the process of logistics, it lacks supervision traceability. Application of Blockchain chain Technology in Logistics and Supply Chain proposes to resolve these problems using blockchain technology. Blockchain makes an application more efficient, reliable and transparent. It also optimizes the entire process. Traceability is an underlying characteristic of blockchain technology. For each event in logistics system, intelligent contract and a blockchain system is constructed to enhance the overall efficiency and supervision. Data collection becomes easier when we correlate the data belonging to same and different blockchain systems. Logistics system can be made more intelligent by constructing different storage and access mechanisms. This project provides a thought on developing a logistics and supplychain application based on blockchain technology.
A Study on the Infographic Design Program through the Individualized Instruction in Specialized High School

Students in the design department of specialized high schools should flexibly learn how to materialize and visualize information along with creativity through individualization. However, due to the various limitations of the classes, there is a uniform classification system that does not consider the diversity of students. Therefore, the researcher proposes learning method using visual design-infographic design along with individualization classes to consider individual differences between students in the design deparment. As a research method, the theoretical background of infographic design and individualization classes was examined through literature research and prior research, and the fusion of infographic design classes and individualization classes was studied. After that, a survey was conducted on design teachers to lay the foundation for the class, and infographic design classes were specified through the concept of individualized classes. The researcher planned a group-specific individualized infographics design classes in 5 stages. The researcher conducted a 3-week (April 11-29, 2022) individualized infographics design classes targeting 62 students in the design department of a specialized high school in Seoul, and proved its effectiveness by obtaining a high satisfaction rating with an average score of 4.5 in a post-classes satisfaction survey.
The Effects of Visual Characteristics of HMD on VR Contents Engagement

There is a growing trend of user demand for highly immersive media that provides a sense of reality. The utility of virtual reality, which is expected to be a promising industry, arises from the interaction process of user interest, participation, and immersion. Therefore, the concept of engagement, which leads to user behavior by eliciting their experiences and interests, is crucial in determining the success of virtual reality content. Effective application of the concept of engagement is necessary at this point. This study aims to analyze the relationship between the visual characteristics of HMD, the sense of reality of virtual reality content, and media engagement to develop efficient user experience design strategies that enhance engagement in virtual reality content. Through a review of previous studies, we derived each characteristic factor and conducted a survey/statistical analysis of the relationships between them. As a result, we concluded that the visual characteristics of HMD have a significant impact on the sense of reality of virtual reality content. Additionally, it was analyzed that interactivity, media invisibility, and current event factors among the factors affecting the sense of reality in virtual reality have a significant impact on immersion, interest, and attention. In the competition of deepening sense of reality-based services, we expect virtual reality technology to actively utilize the engagement factors of virtual reality content and contribute to the development of virtual reality content that integrates various media.
The Impact of Covid-19 on Dental Hygiene and Students' Academic Self-Efficacy Loneliness on Smartphone Overdependence

Objectives: This study aimed to examine the relationships between smartphone addiction, academic self-efficacy, and loneliness among dental hygienist students and use the findings as a basic data to prevent the students’ smartphone addiction.&#x0D; Methods: A total of 232 dental hygienist students attending to School of Dentistry in Seoul, Gyeonggi, or Chungcheong were included in this study.&#x0D; Results: The results of the study showed that loneliness (β=0.202) was contributing the most to the dental hygienist students’ smartphone addiction, followed by academic self-efficacy (β=0.196) and self-reported smartphone addiction (β=-0.178).&#x0D; Conclusions: Further research is recommended on investigation of various factors that influence smartphone addiction during the COVID-19 pandemic, development of counselling centres for the dental hygienist students, and personalized counselling program for prevention of smartphone addiction.
Development of STEAM-based robot education program: Focusing on the case of operation of the 2022 University Career Exploration Camp

Teenagers living in the era of the 4th Industrial Revolution, where technology is developed and academic convergence takes place, need various experiences and a convergence education environment. Therefore, this study is an “Artificial Intelligence Robot Design Experience Group with Metabus” program to operate a university career exploration camp, and a total of 211 middle and high school students participated from May to November 2022. In the satisfaction and survey, more than 93.4% of middle school students and 96.4% of high school students responded positively that they wanted to re-participate in university career exploration camp activities. The research results have embodied the career goals of middle and high school students in the field of convergence of intelligent robot new technologies and serve as a hub for various experiences and convergence education courses for teenagers living in the era of the 4th Industrial Revolution. It is expected that the university career exploration camp program of this study will be used for youth's career in the future, and many studies on university career exploration camps will be conducted as a convergence program between engineering and art.
Transformer in galaxy: a study of galaxy classification based on the Swin transformer

By replicating the CNN part of Galnet network and inspired by Swin Transformer, the article constructs a Transformer-based model called SwinT Galnet by adding the Swin Transformer block into Galnet thus introduces the attention mechanism into that traditional model, trying to investigate the improvement of Transformer over CNN in a single Sérsic galaxy model data. This article compares Sérsic parameters of SwinT with those of CNN. The paper compares the parameters trained by Galnet and SwinT Galnet and shows that the Transformer has an improvement by calculating global attention in accuracy over the pure CNN-based network in galaxy parameters regression tasks, indicates that Transformer has a large potential in the field of galaxy morphology.
A framework for automatic identification of neural network structural redundancy based on reinforcement learning

The increasing structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works usually compress models by removing unimportant filters based on the importance. However, the importance-based algorithms tend to ignore the parameters that extract edge features with small criterion values. And recent studies have shown that the existing criteria rely on norm and lead to similar model compression structures. Aiming at the problems of ignoring edge features and manually specifying the pruning rate in current importance-based model pruning algorithms, this paper proposes an automatic recognition framework for neural network structure redundancy based on reinforcement learning. First, we perform cluster analysis on the filters of each layer, and map the filters into a multi-dimensional space to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we use reinforcement learning to automatically optimize the cluster dimension, and then determine the optimal pruning rate for each layer to reduce the performance loss caused by pruning. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework.