Reviewerly/exHarmony huggingface dataset:
Knowledge and Awareness of Elementary School Teachers about Reading Disability (Dyslexia) in Kingdom of Saudi Arabia, Medina 2018

Introduction: For several years teachers have been concerned about students who appear normal, intelligent, and healthy, but struggle with reading and learning to read and write. These difficulties are identified under the concept of dyslexia [1]. Dyslexia is defined as learning disability that is neurobiological in origin. It is characterized by problems with fluent word recognition and by poor spelling and interpreting skills. Rationale of Study: Dyslexia appears to be the most common learning disability in mainstream schools since the average number of children with dyslexia universally appears to be between 10-15% of the population [19]. Objectives of the Study: To assess Knowledge and Awareness of al-medina elementary school teachers about reading disability (dyslexia). Methodology: Cross sectional study. This study was conducted in al-Medina city. It is located at the north western region of kingdom of Saudi Arabia. Al-Medina city has 208 Elementary female schools and 181 Elementary male schools (published in ministry of education website 1438 The study population included female and male Saudi teachers in primary schools in Al-Medina the total population (Saudi female and male teachers in primary schools) 10,522 expected frequency =50% at 95% confidence level the sample size =371 (calculated by using survey software A self-reported questionnaire was give to primary schools teachers. Results: A total of 380 teachers of elementary school in Madinah city, Saudi Arabia were included in the study. The personal characteristics of the studied subjects are presented in Table 1. The mean (±SD) age of the studied teachers was 40.6(±6.2) years and age ranged from 25-59 years. About 41.6% were above the age of 40 years and about half of the studied teachers were males (51.3%). The majority were married (92.6%), and had more than 3 children. The majority had bachelor degree (72.6%). Almost all the studied teachers reported to have monthly income of more than 8000 SR (97.1%). More than half of the studied teachers were teaching Arabic and Islamic subjects (56.8%), 22.4% of the teachers were teaching science and Mathematics. Teachers with work experience &gt; 10 years were representing 58.2% of the studied sample with the mean years of experience was 8.4 ± 4.6 (1-19 years). Conclusions: The present study revealed that good level of knowledge on dyslexia among the studied elementary school teachers in Madinah City to account (42.9%). The results showed also that the majority of teachers to have positive opinion about the need of educational program and special training courses on dyslexia. Although not significant, good level of knowledge on dyslexia was varied by the socio-demographic characteristics of the studied teachers. The higher level was among teachers aged ≤ 40 years, male teachers, married teachers, teachers having 1-3 children, teachers having bachelor degree. The highest percent of good knowledge was found among science and mathematics teachers.
Investigating Pre-service Mathematics Teachers’ Perception on Cognitive Needs

This article reports on part of a two- year study. The paper examined the perception of pre-service mathematics teachers’ cognitive needs via the lens of Maslow theory of needs. The research design employed was the mixed methods approach specifically sequential explanation mixed methods design. The population for this study was pre-service mathematics teachers at the University of Education, Winneba of Ghana. The purposive sampling technique was employed to select Level 300 pre-service mathematics teachers. Level 300 students were 183. The simple random sampling technique was used in selecting 125 respondents for the study. Questionnaire and interview protocol were used as instruments to collect data. Data collected through the questionnaire were analysed quantitatively and the interview data collected were analysed qualitatively. Results showed that the cognitive needs of the pre-service mathematics teachers have been met to a very large extent. Recommendations were thereof made accordingly.
EFFICIENCY SUBSTANTIATION FOR A SYNTHETICAL METHOD OF CONSTRUCTING A MULTIVARIATE POLYNOMIAL REGRESSION GIVEN BY A REDUNDANT REPRESENTATION

In recent years, the authors in their publications have developed two different approaches to the construction of a multivariate polynomial (in particular, linear) regressions given by a redundant representation. The first approach allowed us to reduce estimation of coefficients for nonlinear terms of a multivariate polynomial regression to construction of a sequence of univariate polynomial regressions and solution of corresponding nondegenerate systems of linear equations. The second approach was implemented using an example of a multivariate linear regression given by a redundant representation and led to the creation of a method the authors called a modified group method of data handling (GMDH), as it is a modification of the well-known heuristic self-organization method of GMDH (the author of GMDH is an Academician of the National Academy of Sciences of Ukraine O. G. Ivakhnenko). The modification takes into account that giving a multivariate linear regression by redundant representation allows for construction of a set of partial representations, one of which has the structure of the desired regression, to use not a multilevel selection algorithm, but an efficient algorithm for splitting the coefficients of the multivariate linear regression into two classes. As in the classic GMDH, the solution is found using a test sequence of data. This method is easily extended to the case of a multivariate polynomial regression since the unknown coefficients appear in the multivariate polynomial regression in a linear way. Each of the two approaches has its advantages and disadvantages. The obvious next step is to combine both approaches into one. This has led to the creation of a synthetic method that implements the advantages of both approaches, partially compensating for their disadvantages. This paper presents the aggregated algorithmic structure of the synthetic method, the theoretical properties of partial cases and, as a result, the justification of its overall efficiency.
АЛГОРИТМ ВИМІРЮВАННЯ ПОДІБНОСТІ МІТОК ДІЯЛЬНОСТЕЙ НА ОСНОВІ NLP У БАЗІ ДАНИХ МОДЕЛЕЙ БІЗНЕС-ПРОЦЕСІВ

Business process modeling is an important part of organizational management since it enables companies to obtain insights into their operational workflows and find opportunities for development. However, evaluating and quantifying the similarity of multiple business process models can be difficult because these models frequently differ greatly in terms of structure and nomenclature. This study offers an approach that uses natural language processing techniques to evaluate the similarity of business process models in order to address this issue. The algorithm uses the activity labels given in the business process models as input to produce textual descriptions of the associated business processes. The algorithm includes various preprocessing stages to guarantee that the textual descriptions are correct and consistent. First, single words are retrieved and transformed to lower case from the resulting textual descriptions. After that, all non-alphabetic and stop words are removed from the retrieved words. The remaining words are then stemmed, which includes reducing them to their base form. The algorithm evaluates the similarity of distinct business process models using similarity measures, including Jaccard, Sorensen – Dice, overlap, and simple matching coefficients, after the textual descriptions have been prepared and preprocessed. These metrics provide a more detailed understanding of the similarities and differences across various business process models, which can then be used to influence decision-making and business process improvement initiatives. The software implementation of the proposed algorithm demonstrates its usage for similarity measurement in a database of business process models. Experiments show that the developed algorithm is 31% faster than a search based on the SQL LIKE clause and allows finding 18% more similar models in the business process model database.
Pengaruh Partisipasi Orang Tua terhadap Motivasi Belajar Siswa Kelas V SDN 023 Pandau Jaya Kabupaten Kampar

The learning motivation of students at SDN 023 Pandau Jaya, Kampar Regency, is still relatively low because some of the students whose parents participate are not optimal both physically and non-physically. This study aims to determine whether there is an influence of parental participation on the learning motivation of fifth grade students at SDN 023 Pandau Jaya, Kampar Regency. This type of research uses a quantitative approach to the ex-post facto method. The population of this study was 87 students of class V with the sampling technique using the non-probability sampling method, namely saturated sampling. So a sample of 87 students consisted of 46 boys and 41 girls. The data collection technique used a questionnaire and was processed using a correlation test with the help of SPSS Version 24. The results showed that based on the calculation of the research hypothesis that had been carried out, it was found that the value of tcount &gt; ttable (7,580 &gt; 1,980), then Ha was accepted, Ho was rejected. Based on this, it can be concluded that there is an influence between parent participation on the learning motivation of fifth grade students at SDN 023 Pandau Jaya, Kampar Regency.
Pengaruh Kebiasaan Literasi Siswa terhadap Kemampuan Membaca pada Pembelajaran Bahasa Indonesia Kelas 2 SDN Benda 2

The educational process in schools learning activities are the most basic activities. This means that the success or failure of achieving educational goals as stipulated in the law of the Republik of Indonesia number 20 0f 2003 concerning the national education system, namely”To develop the potential of students to become human being eho believe in and fear god almighty, have noble character, are healthy, knowledgeable, capable creative, independent, and become citizens of a democratic and responsible state.” Very much depends on how the learning process experienced by students as learners. Like a war in learning, students must also be prepared. Namely knowing what things help the success of learning and what often makes th lesson fail. So that a student must understand good study techniques, know the right time to learn to manage time and be disciplined in learning as well as getting used to reading and visitimgm the library which is a storehouse of all reading materials.By carrying out good habits in learning, a student will get high achievement and finally be successful I learning.
Application and development of artificial intelligence in environmental health

In recent years, major industries have been hit hard by the COVID-19 pandemic, and people have become more aware of issues such as environmental health. The development of computer programs, artificial intelligence (AI), has gradually entered the vision of researchers. The product of this technology can help people better manage and improve the current environmental conditions. This paper, through a method of literature review, aims to explain the development history of AI and its application in different fields such as the environment. Through the research, the paper concludes that artificial intelligence can observe in real time for long periods of time without getting tired like humans do. Based on the application of big data analysis and intelligent programs, artificial intelligence can make reasonable plans in a much shorter time than human beings. However, the solution is only based on the operation of the system. In the actual situation, there will be many obstacles. Therefore, it is still necessary for humans to make judgments based on the results of an operation rather than relying entirely on artificial intelligence. Complicated weather conditions can also make the results of the calculation inaccurate. So there will still be a need to evolve computer programs or for artificial intelligence to be able to calibrate the data it is monitoring from the past.
A Review of the Test Items of the LPATE From the Perspective of the CEFR’s ‘Can-Do’ Descriptors

This research was conducted to analyze the content validity of the Language Proficiency Assessment for Teachers (English Language) (LPATE) adapted to the ‘can-do’ descriptors of Common European Framework Reference for Languages (CEFR). Through descriptive qualitative research, the assessment questions were analyzed using the validity formula of Suharsimi Arikunto and Pearson. The results showed that the LPATE has good validity and is following the CEFR’s ‘can-do’ descriptor. Although some of the items discussed things that were not following the ‘can do’ statement on the CEFR, these did not interfere with the validity of the test because the material and types of questions given were in line with the final competency achievement of the LPATE.
Banking Malware Attacks and Security Solutions Review

This research explores the growing threat of banking malware attacks and the security solutions that can be implemented to mitigate the risks. The paper begins by providing an overview of the different banking malware attacks, including their propagation methods and the damage they can cause. It then delves into the various security measures that can be taken to prevent and detect these attacks, such as endpoint protection, network segmentation, and user education. The paper also examines the challenges and limitations of these security solutions and the potential for future developments in the field. Overall, this paper provides a comprehensive analysis of the current banking malware attacks and the security solutions that can be employed to safeguard against them. This research aims to comprehensively analyse the different types of banking malware attacks and the security solutions that can mitigate the risks. By understanding the nature of these attacks and the effectiveness of various security measures, this research can help financial institutions develop more effective strategies for protecting themselves and their customers from cyber threats.
Parallel Bootstrap-Based On-Policy Deep Reinforcement Learning for Continuous Fluid Flow Control Applications

The coupling of deep reinforcement learning to numerical flow control problems has recently received considerable attention, leading to groundbreaking results and opening new perspectives for the domain. Due to the usually high computational cost of fluid dynamics solvers, the use of parallel environments during the learning process represents an essential ingredient to attain efficient control in a reasonable time. Yet, most of the deep reinforcement learning literature for flow control relies on on-policy algorithms, for which the massively parallel transition collection may break theoretical assumptions and lead to suboptimal control models. To overcome this issue, we propose a parallelism pattern relying on partial-trajectory buffers terminated by a return bootstrapping step, allowing a flexible use of parallel environments while preserving the on-policiness of the updates. This approach is illustrated on a CPU-intensive continuous flow control problem from the literature.
Special Issue on New Advances and Illustrations in Applied Geochemistry in China

The 9th national conference on applied geochemistry in China will be held in Chengdu, Sichuan province, in October 2023, hosted by the committee of applied geochemistry, the Chinese Society for Mineralogy, Petrology and Geochemistry (CSMPG) [...]
You Watch Once More: A More Effective CNN Architecture for Video Spatio-Temporal Action Localization

Abstract The task of spatio-temporal action localization (STAL) needs to detect the action and position of individuals in the scene. Many works focus on how to improve the accuracy, but they usually ignore inference speed and practical applications. To address the above problems, we propose a new end-to-end spatio-temporal action localization network called You Watch Once More (YWOM). In this work, there are three measures proposed to improve the accuracy of positioning and recognition while guaranteeing the inference speed. First, a new feature fusion mechanism based on frequency channel attention (FCA) is proposed, which can effectively fuse the features extracted by different backbones. In addition, a new loss function is proposed to speed up the regression and convergence of the bounding box. Specifically, the SIOU regression loss function instead of the smooth L1 loss function is applied to help the model converge stably. Moreover, a lateral connection mechanism is designed to apply more backbones to our network structure. The experimental results demonstrate that YWOM can achieve online inference speed and has good performance of spatio-temporal action localization tasks. YWOM has superiority over other related works including YOWO on the UCF101-24 dataset. The frame-mAP and the video-mAP (0.2) are improved by 4.23% and 1.63% respectively.
Digital Expressions: Exploring Cultural Emoticons and Changing Digital Speech Patterns

&lt;p&gt;With the development of technology and computing applications, communication among people become easy. Nowadays different platforms are accessible for expressing news, knowledge, and multiple emotions such as happiness, sadness, disappointment, thoughts, opinions. In the current digital world, emoticons play a vital role in computer-based communication. This study examined the usage of emoticons and the slight shift of words to emoticons in computer-mediated communication. The research also investigates how people understand the emoticon, and used them for clearing their meanings. The study investigates participants’ views and interpretations of emoticons by utilising a mixed-methods technique that combines surveys and qualitative analysis of communication data. It investigates emoticons’ cultural relevance and communication potency while taking into account variables like age, gender, and social environment that affect emoticon usage. The main focus of the study is to identify the demand for emoticons from a cultural perspective. The findings have consequences for people, academics, and practitioners, highlighting how crucial it is to take cultural considerations into account when creating communication platforms and comprehending how visual symbols affect message perception. In addition to this study, the frequency usage of emoticons is also investigated.&lt;/p&gt;
FrequentNet: A frequency-based neural network architecture with joint temporal and frequency domains

&lt;p&gt;The frequency domain plays a crucial role in image processing. However, modern neural networks, such as Convolution Neural Networks and Transformers, only operate in the temporal domain, resulting in a contradiction concerning information aggregation. In contrast, the frequency domain has distinct advantages to solving the contradiction. In this paper, we introduce a frequency-based neural network architecture with joint temporal and frequency domains named as FrequentNet. We analyze the challenges in frequency-based neural networks associated with combining temporal and frequency domain information. Moreover, we find that the absence of frequency-domain downsampling methods and complex computations also affect the frequency models' performance. To tackle the abovementioned problems, we introduce a residual connection that separates the temporal and frequency domains to resolve information aliasing. Furthermore, we devise a frequency domain down-sampling method based on the mapping. Finally, we use Discrete Cosine Transform as the frequency domain transformation operator to avoid the need for complex computations. Comprehensive experiments demonstrate that our approach surpasses existing frequency-based backbones in diverse fields, including image classification, object detection, and semantic segmentation, whose superiority stems from the frequency domain's robust and efficient information aggregation capability.&lt;/p&gt;
Deep Convolutional Neural Network System Based Classification of Digital Mammograms

Abstract Breast cancer is very common and considered as the second dangerous disease all over the world due to its mortality rate. So, if the detection is early enough, it can reduce the death rate. Image processing techniques are applied to accurately segment the Region of Interest (ROI) prior to abnormality detection in digital mammograms. The digital mammograms can majorly classify into two types, normal and abnormal. Abnormal cases are taken for further process. In this paper, some of the Non-linear techniques are applied to the mammogram images for the removal of noise at pre-processing. Noise removal is done by using lee filter, frost filter, median filter and improved statistical based bilateral filter. The best filter is selected by measures of MSE, PSNR and SSIM. Watershed, K-means clustering algorithms has been applied to the filtered image to get the accurate result prior to segmentation. For feature extraction, features from mammogram are calculated by using GLCM and Tetrolet Transform. Deep Convolutional Neural Network (DCNN) is used for classification of the images. Mammogram from MIAS and DDSM database is taken for simulation.
Ten Years of Malang Confucius Institute in Promoting Chinese Language and Culture in Indonesia Higher Education: Its Development and Challenge

The development of Chinese language learning in Indonesia, which is currently very rapid, isa result of the hard work carried out by the Confucius Institute (CI). Within ten years, the CI has spread its wings to various regions of Indonesia, including Malang. CI Malang was founded in collaboration between Universitas Negeri Malang in Indonesia and Guangxi Normal University in China. The CI Malang became a branch of the CI established by Guangxi Normal University overseas. Although the impact that CI has had on the Chinese language and culture is great, there are several challenges faced by CI, such as the difficulties adapting to Indonesia’s diverse culture, the absence of a Chinese learning curriculum owned by Indonesia, and differences in the digitalization system Indonesian and Chinese. These challenges can motivate CI to successfully adapt to local conditions and fully play its role as a language and culture bridge to achieve sustainable and innovative development and build a better platform for cultural exchanges and friendly exchanges between China and Indonesia.
Determinant of Lecturers’ Research Productivity: A Comparative Study Between Universitas Negeri Surabaya (UNESA) and UiTM Malaysia

This study aims to examine the effect of motivational variables, research behavior, research climate, and university support on the research productivity of lecturers at the Faculty of Economics and Business Unesa and UiTM Malaysia. Data was collected through questionnaires to lecturers from these universities. The results showed that motivation had a positive and significant effect on the research productivity of lecturers at the Faculty of Economics and Business, Unesa. Research behavior has a positive and significant effect on the research productivity of lecturers at the Faculty of Economics and Business, Unesa. The research climate has a positive and significant effect on the research productivity of lecturers at the Faculty of Economics and Business, Unesa. University support has a positive and significant influence on the research productivity of lecturers at the Faculty of Economics and Business, Unesa. Motivation, research behavior, research climate, and university support together have a significant positive influence on the research productivity of lecturers at the Faculty of Economics and Business, Unesa.