Reviewerly/exHarmony huggingface dataset:
ENGLISH WRITING INSTRUCTION AND STRATEGIES AS PREDICTORS OF WRITING ANXIETY OF STUDENTS

This study aimed to determine English teachers’ writing instruction and students’ writing strategies significantly predict students’ ESL writing anxiety. The working group for the present study, in which quantitative descriptive-predictive design was employed, included 271 first-year students enrolled in GE 2 subject at UM Digos College. The participants answered adopted standard questionnaires to quantify which dimension/s of English Teachers’ writing instruction and English students’ writing strategies significantly predict/s writing anxiety. The gathered data were analyzed and interpreted using the mean and standard deviation, Pearson-r, regression, and multiple linear regression. Regression analysis was used to predict the value of writing anxiety based on the value of writing instruction and strategies. Multiple linear regression was used to model the linear relationship between writing anxiety and writing instruction and strategies. The results revealed that teachers’ explicit writing instruction in teaching and students’ employment strategies in writing helped the learners lessen their writing anxiety. The findings corroborated the three domains for English writing instructions and English writing strategies: learning to write factual texts, learning to write formal and informal texts, and resourcing, significantly predict English writing anxiety. The findings obtained in this study suggest that the employment of effective instruction and strategies for students may alleviate anxiety and desirably perform various writing tasks in any particular genre.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0908/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Research on the Teaching Reform of "PHP Programming" Course based on Case-based Teaching

In the traditional teaching mode, there are problems that programming language courses tend to be theory-oriented and students face uneven programming foundations. Based on the problems that the course content is too theoretical and the students' programming foundation is different, the teaching reform research is carried out. Taking the course of "PHP Programming" as an example, this paper proposes a case-based teaching reform of "PHP Programming". This paper firstly analyzes the teaching status of "PHP Programming", then expounds the significance of the teaching reform of "PHP Programming" based on case-based teaching, and finally puts forward the teaching reform strategy of "PHP Programming" based on case-based teaching. Transmit more applied talents for the society.
Application of Certainty Factor Method in Intelligent System for Diagnosis of Periodontal Disease in Cigarette AddictsApplication of Certainty Factor Method in Intelligent System for Diagnosis of Periodontal Disease in Cigarette Addicts

This study discusses the design of an Android-based innovative application that is useful in the early diagnosis of periodontal disease in cigarette addicts. The problem which is the main topic of discussion in this study is the health issue of organs that are often ignored by humans, namely the teeth and mouth. Then one of the types of dental and oral diseases that many people often complain about is the periodontal disease which also gets less attention in Indonesian society. And one of the causes of this periodontal disease is caused by smoking habits. So to facilitate the identification in knowing the periodontal symptoms caused by the cigarette, a system that can identify the early symptoms of periodontal disease is needed. The technology proposed to build the system applies expert system technology with Certainty Factor. In building an android-based innovative application to diagnose periodontal disease, there are cigarette addicts in this study with the Research &amp; Development research method to produce an output that can be right on target by the expected target. In addition, interviews and direct observation techniques were also carried out with experts or experts in the field of dental and oral diseases to collect the required data on the needs of the system to be built.
Data Mining implementation on SMUN Scholarship recipient candidates using the C4.5 algorithm

This research is motivated by SMUN previously only providing scholarships for underprivileged students, while for students who excel, the method is not yet, therefore, research by implementing data mining with the C4.5 algorithm method so that in the future, if the scholarship program is for outstanding students already exists, then SMUN can immediately apply it. The sample data are 33 scholarship recipients with subjects that have been determined based on the report card scores of prospective scholarship recipients. The selection results for receiving scholarships to outstanding students so far have only been calculated in general terms, the possibility of incorrect results will cause losses for students who have good achievements. As a result, there are students who should get the scholarship rights and do not receive it. The problem of this research is how to make it easier to analyze students who need and are entitled to receive scholarships, how to make it easier to select and determine scholarship recipients. The research uses the C4.5 algorithm to overcome these problems. With the C4.5 algorithm, the percentage accuracy value can be calculated which is used to determine whether the student is entitled to receive a scholarship or not. The data collection technique in this study is to use student report cards. classification value in determining students who get scholarships with the highest accuracy of 81.81%. The accuracy value was obtained by experimenting with the testing process with the number of student data as many as 33 students. It is hoped that it will make it easier to receive scholarships for students who excel academically in school.
Implementation of Generative Pre-Trained Transformer 3 Classify-Text in Determining Thesis Supervisor

One of the requirements for graduating from the undergraduate level for universities in Indonesia is writing a final project or thesis. In order to graduate, of course, it is greatly influenced by the desire and strong spirit of the students and also the guidance of the supervisor. In determining the supervising lecturer, special attention must be paid to the field. Usually the selection of lecturers for thesis supervisors is determined by the study program through a meeting of lecturers in order to determine which lecturers are considered according to the title of the student and in accordance with the research of the supervisor. However, this method is a bit inconvenient and also quite time-consuming considering the number of students is more than a hundred and continues to grow every year. In this study, the thesis supervisor was classified based on the title proposed by the student. The methodology that will be used in this research is the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology whose stages are: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and deployment, as well as using Generative Pre-Technology. trained Transformers 3 (GPT-3)
Digital Authoritarianism and The Global Assault on Human Rights

Across the world, governments and state-aligned actors increasingly target human rights defenders online using techniques such as surveillance, censorship, harassment, and incitement, which together have been termed “digital authoritarianism.” We currently know little about the concrete effects on human rights defenders of digital authoritarianism as researchers have focused primarily on hate speech targeting religious, national, and ethnic minority groups. This article analyzes the effects of digital authoritarianism in two countries with among the highest rates of killings of human rights defenders in the world; Colombia and Guatemala. Anti-human rights speech in these countries portrays defenders as Marxist terrorists who are anti-patriotic and corrupt criminals. Evidence for a direct causal link to offline violence and killing is limited, however, and this empirical study documents the non-lethal and conditioning effects of speech. Human rights defenders who are targeted online report negative psychological and health outcomes and identify a nexus between online harassment and the criminalization of human rights work. Many take protective measures, engage in self-censorship, abandon human rights work, and leave the country. To prevent these harms, social media companies must implement stronger human rights-protective measures in at-risk countries, including expediting urgent requests for physical protection, adopting context-specific content moderation policies, and publicly documenting state abuses. The article concludes by advocating for a new United Nations-sponsored Digital Code of Conduct that would require states to adopt transparent digital policies, refrain from inciting attacks, and cease illegally surveilling human rights defenders.
Modelling Ecosystems of Pedagogical Education in the Postnonclassical Paradigm

The problem of modelling open systems in education, in which non-linear processes take place, requires the use of postnonclassical methodology. Reflection of the reality of pedagogical education which exists in the form of an ecosystem of formal institutions, urban educational formats, educational platforms and communities of practice playing an important role in the development of a future teacher, is possible when building a dynamic model for the development of a professional community and a teacher in it. Research methodology and methods: social constructionism and synergetics as a general scientific methodology of postnonclassical science, analogy, action research, interviews, network analysis, modelling, mathematical description of a model. The purpose of the article is to characterize the application of modelling as a particular methodology in the postnonclassical paradigm of the study of pedagogical education and to propose a prototype of mathematical description of the local (regional) ecosystem of open pedagogical education. The results were obtained during constructing the model in a temporary research team by modelling in action (by the Action Research type) on the local system of engineering and technology education in Krasnoyarsk. Attractors-competences and lead functions have been identified, mathematical coefficients have been developed to track the dynamics of the development in the ecosystem of pedagogical education based on the growth of diversity in the system. In conclusion, specific characteristics of modelling pedagogical education in the postnonclassical paradigm have been highlighted.
RESEARCH ON DIGITAL IMAGE PROCESSING TECHNOLOGY AND ITS APPLICATION

The improvement of pictorial information for human interpretation and the processing of image data for storage, transmission, and representation for autonomous machine perception are the two main application areas that have sparked interest in digital image processing techniques.This article's goals are to define the terms "image processing" and "image processing scope," "typical image processing procedures and methodologies," and "applications of image processing tools and processes in the frontier areas of study.
Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning

Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by $$15.79\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>15.79</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for the SPL and by $$23.83\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>23.83</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN .
Student’s digital competences in Belgium and Romania: A comparative analysis

This research explores students’ digital competences level in three major universities from Belgium and Romania, based on a combination of seven core skills: communication and collaboration skills, creativity skills, critical thinking skills, information skills, problem-solving skills and technological skills. We applied a quantitative design based on an online survey, applied during March 2021–May 2022 using Confirmatory Factor Analysis. The study focuses on examining the qualifications and implementation of students’ digital abilities in Romania and Belgium, looking for similarities and differences. The results indicate that students from Belgium have higher average scores for communication and collaboration, information, and problem-solving digital skills than the other skills, while, in the case of students from Romania, creativity and technological skills have the highest average scores compared to the other digital skills. In addition, significant differences between countries regarding the average scores of critical thinking digital skills were not found. Although having a basic understanding of digital competences is advised, improved policies are required to promote the development of these skills as the skill sets needed for information and communication technology employees are more demanding.
State of the Art of Cybersecurity in Cooperative, Connected and Automated Mobility

In recent years, mobility systems have been steadily improving their performance in terms of connectivity, allowing road users to communicate among them and with the road infrastructure either directly or through internet cloud-based services. The aim of this contribution is to provide an overview of inter-vehicle communications cybersecurity challenges in the cooperative, connected and automated mobility sector and make possible a further analysis of the cryptographic methods and tools that can leverage their security in a world where quantum computers will be a practical menace at some point in the future.
THEORETICAL AND RESEARCH ASPECTS OF THE PROBLEM OF IMPROVING THE PERSONAL AND PROFESSIONAL SPHERE IN THE TEACHER'S PEDAGOGICAL ACTIVITY

The relevance of the outlined problem is determined by modern innovative transformations in the field of education. The article presents theoretical and research aspects of the problem of development and improvement of personal and professional spheres in the teacher's pedagogical activity. The role of the teacher's personal sphere is substantiated, pedagogical orientation in particular, socially and professionally the most significant personality traits are singled out, namely: social responsibility, love for the child, love for one's profession, humanism, benevolence, sociability; corresponding professional qualities include: theoretical and methodical training by profession, psychological-pedagogical and theoretical-practical preparation, development of pedagogical skills. An important role in the self-improvement of a teacher is played by such a cognitive component as knowledge that forms the foundation of continuous self-development and professional and pedagogical education. The philosophy of anthropocentrism as a new quality of innovative thinking of a person, which surpasses the established standards of mind frame of past eras, appears as the methodological basis of knowledge about a person. An activity component has been identified, which includes pedagogical skills and mastery of the methods and techniques of teaching and upbringing, based on the conscious use of psychological-pedagogical and methodical knowledge. The active component includes such components as gnostic, projective, constructive, communicative, organizational. The research aspect involves conducting a pedagogical experiment to identify, in particular, the dynamics of the formation of the components of the activity component in the process of training future teachers in higher education institutions. As a result of the experiment, through the introduction of the appropriate model and technology, there were changes in the levels of formation of professional skills in educational work according to students' self-assessment. The graduates' orientation towards further professional self-improvement and self-development is also traced. All this testifies to the students' readiness for future independent work to improve their personal and professional qualities, knowledge, and skills. A comparative analysis of the data obtained at the beginning and after the completion of the formative experiment made it possible to identify positive changes in the levels of productivity of students' training to improve the personal and professional spheres of students in the experimental and control groups. Thus, the analysis of scientific literature and the conducted experiment testify to the effectiveness of the developed model and technology in improving personal and professional spheres in the pedagogical activity of an elementary school teacher. Several problems have been identified that require further research by taking into account the current situation in Ukrainian society: the pandemic and martial law.
Motion-region annotation for complex videos via label propagation across occluders

Abstract Motion cue is pivotal in moving object analysis, which is the root for motion segmentation and detection. These preprocessing tasks are building blocks for several applications such as recognition, matching and estimation. To devise a robust algorithm for motion analysis, it is imperative to have a comprehensive dataset to evaluate an algorithm’s performance. The main limitation in making these kind of datasets is the creation of ground-truth annotation of motion, as each moving object might span over multiple frames with changes in size, illumination and angle of view. Besides the optical changes, the object can undergo occlusion by static or moving occluders. The challenge increases when the video is captured by a moving camera. In this paper, we tackle the task of providing ground-truth annotation on motion regions in videos captured from a moving camera. With minimal manual annotation of an object mask, we are able to propagate the label mask in all the frames. Object label correction based on static and moving occluder is also performed by applying occluder mask tracking for a given depth ordering. A motion annotation dataset is also proposed to evaluate algorithm performance. The results show that our cascaded-naive approach provides successful results. All the resources of the annotation tool are publicly available at http://dixie.udg.edu/anntool/ .
Wave-like patterns in parameter space interpreted as evidence for macroscopic effects resulting from quantum or quantum-like processes in the brain

Data from eight numerosity estimation experiments reliably exhibit wave-like patterns in plots of the standard deviations of the response times along the abstract parameter of the magnitude of the error in the numerosity estimation. An explanation for this phenomenon is proposed in terms of an analogy between response times and error magnitude on one hand, and energy and position of quantum particles on the other, constructed using an argument for an overlap between the mathematical apparatus describing Hopfield-type neural networks and quantum systems, established by some researchers. Alternative explanations are presented within the traditional explanatory framework of oscillations due to neural firing, involving hypothetical mechanisms for converting oscillation patterns in time to oscillation patterns in the space of an abstract parameter, such as the magnitude of the error during numerosity estimation. The viability of the proposal of causal influences propagating from the microscale of quantum phenomena to the macroscale of human behavior, needed for the first type of explanation, is exemplified by the phenomenon of magnetoreception in some species of birds, which is allegedly quantum in nature.
Investigation of the Optimal Sensor Location and Classifier for Human Motion Classification

Human motion monitoring by means of wearable technologies is not uncommon nowadays. This demonstrates the growing awareness of the importance of healthy lifestyle. Human body motion involves the movement of multiple muscles and joints. However, the optimal location of sensor placement on the body to record the motion in daily activities has not been well understood. This study aims to find the best sensor location for this purpose among three locations on the body, that is on the back, shank, or wrist. In addition, this study seeks to find the best classification algorithm for human daily activities. The data recorded at these three locations were analysed using several classification algorithms in both Orange software and MATLAB. The results show that the sensor on the wrist provided the best classification result, thereby suggesting that wrist is the best place on the body to place the sensor for human motion monitoring. With regards to classification algorithm, we found that Neural Network provides the most accurate classification as compared to other algorithms. Future development of wearables should look into integrating classification algorithm in the system, thus the human motion monitoring will provide a richer information and not only limited to number of steps and calories burned.
Forensic Face Sketch Recognition based on Pre-Selected Facial Regions

In law enforcement, face sketch recognition has been used to identify the criminal suspect. Usually, when there is no other evidence, a forensic artist will draw the face of the suspect based on the eyewitness description. Then, the forensic sketch will be matched with the mugshot images from the database in order to recognize and identify the potential suspect. However, the matching performance of the forensic sketches could be affected by various factors, and one of the major factors is the occlusion that exists in the sketch itself. This is because most of the suspects usually wear something that could help in hiding their identities, like a face mask, glasses, hoodie, or cap, when they are committing a crime. Since the mugshot images do not include the occlusion, it will make it harder to recognize the suspect in the matching process, even if the sketch and photo are from the same person. This is due to the larger Euclidean distance between the extracted features from these two images, particularly in the occlusion regions. Therefore, this study proposed a method that matches only the pre-selected regions that exclude occlusion in both images. This region of interest is pre-selected on the forensic face sketch before the same region is applied to all mugshot images. In this study, the forensic sketch with their corresponding photo was obtained from the PRIP-HDC dataset, and the Histogram of Gradient (HOG) was used for feature extraction. Based on the result obtained, this study's performance shows some improvement in recognizing the forensic sketches compared to the existing technique.