Reviewerly/exHarmony huggingface dataset:
Business Talent Demand Prediction Model Based On Neural Network in Integration of Industry and Education in Greater Bay Area

With the acceleration of economic development and scientific progress, the accurate prediction of business talent demand is important to promote regional economic improvement and effective planning of education and training. However, traditional forecasting methods face a series of challenges in the complex and changeable environment of the Greater Bay Area. Therefore, in this study, a new business talent demand prediction model was proposed using a neural network algorithm. First, a large amount of historical data was collected and preprocessed to extract features and build a training set of neural network models. Then, this model was used to forecast the demand for the business workforce. The results showed that the business talent demand forecasting model based on the neural network forecasted accurate demand for the workforce in the Greater Bay Area. The results are of great significance for optimizing the training and career planning of students majoring in business and supporting economic improvement.
Solving Maximum Independent Set Problem Using Analog Neutral Atom Quantum Computers

We used an analog neutral atom quantum computer, known as Aquila to solve the maximum independent set problem for a given graph through the Rydberg blockade effect. Aquila, developed by QuEra, is a 256-qubit analog quantum computer designed to operate at room temperature, employing neutral rubidium-87 atoms as its basis for qubit construction. It offers the flexibility to design qubit topologies and dynamically control the physical properties of individual qubits to simulate various quantum systems. By accessing Aquila through the Amazon Braket service, experiments are conducted to determine the maximum independent sets of various king’s graphs. The experimental results were analyzed. Suggestions were given for future research directions in the field of analog neutral atom quantum computers.
Pseudo-random Image-Scrambling Patterns Generated By Direct Sequences

We present a new method for image scrambling that mitigates continuous data loss, or erasures during data transmission or storing/retrieving processes. In transmission or storing, the scrambling process disperses erasures randomly across the whole image. Even without further processing, the dispersion makes the image more readable than the unscrambled. The proposed method is a 2D mapping scrambling technique. We utilized n-bit linear-feedback shift register (LFSR) to generate pseudo-random sequences in 2D coordinates mappings. However, the mapping originates from the content of the n-bits LFSR. These n-bits contents of LFSRs form binary numbers ranging from 1 to $2^{n}-1$, inclusively. We researched LFSRs to generate the maximal-length sequence or m-sequence since m-sequences have the maximal period of $2^{n}-1$, where n is the length of LSFRs. As a result, contents from these LFSRs also have a non-repetitive pattern and a maximal period of $2^{n}-1$. To verify the method, we simulated and compared the performance of scrambling using m-sequences LSFRs and random mappings. Since it was difficult to quantify how comprehensible an image is, we used two metrics to roughly compare different scrambling methods. The minimum distance was defined as the 2D Euclidean distance from one erasure to another erasure closest to the former erasure. Then, we calculated the average of these distances for all dispersed erasures. The other was the percentage of dispersed erasures that were adjacent, or connected, to other erasures. We minimized this metric since continuous erasures after descrambling resulted in an image that was difficult to comprehend. We confined images with 2n pixels so the mapping was easy to establish. We used images of any size and modified the algorithm to perform scrambling and descrambling.
Bi-modal email spam detection using recurrent and convolution neural network techniques

The increasing adoption of electronic emails as a means of communication, both at the commercial, government, and individual levels, serves as an impetus for attackers to compromise communication. Consequently, numerous machine learning techniques have been developed for identifying unwanted emails, commonly known as spam. Despite the significant progress reported in existing literature, most studies do not integrate the detection of both textual and image-based spam. In this paper, two deep learning techniques that detect both textual and image-based spam were evaluated. First, the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) is studied, training them on various textimage features to explore their effectiveness on an improved dataset. Subsequently, in an effort to outsmart current spam detection techniques, a bi-modal architecture capable of detecting textual spam, image spam, and mixed spam is designed. The experimental results in conjunction with existing transfer learning for effective spam detection is provided.
Information Systems Security Policy Framework for enhanced ICT Governance in Public Institutions of Tanzania

This study developed an Information Systems Security Policy Framework relevant in governing Information and Communication Technologies (ICT) in public institutions of Tanzania. It used higher learning institutions as the case for study, The framework is to guide professionals on how to secure ICT environment. Operationally, this study used a qualitative approach. It began with a review of the literature, followed by a focus group discussion to formulate new themes for the proposed Information System Security policy framework. The output of the study suggests a policy framework with the following themes: Data and information handling, Internet and network Services Governance, the use of company-owned devices, physical security, guidelines on how to acquire new hardware and software, incident handling and reporting, monitoring and compliance, and policy administration. This study recommends the use of a new comprehensive and harmonised Information Systems Security policy framework for all public higher education institutions, for a more secure environment. In addition, the study recommends additional studies including other types of organisations for comparison.
PENERAPAN STRATEGI PBL DENGAN MEDIA MABERDI UNTUK MENINGKATKAN TANGGUNG JAWAB DAN DISIPLIN PESERTA DIDIK

The purpose of this development is to increase discipline and responsibility students with PBL strategies by MABERDI learning media. The subjects used were 30 students in class Ic UPT SD Negeri 06 Gresik who were used on the recommendation of the teacher. The media used is a type of board game media, a combination of monopoly and snakes and ladders that has been modified. Based on the research results obtained from quantitative data, namely from the average score of the student discipline attitude and responsibility questionnaire obtained through the questionnaire, the average score was 4.6, categorized as very good. Supported by qualitative data obtained from interviews, it also explains that students experience changes in attitudes of discipline and responsibility. Supported by the results of observations carried out for three days which obtained average results which increased every day. From these data it can be concluded that the use of PBL strategies with the help of MABERDI media can improve discipline and responsibility students.
The Prediction Factors of the Career Development Competencies and Self-Directed Learning Competencies of Vocational High School Students Using Latent Profile Analysis

The purpose of the study is to estimate the latent classes of vocational high school career development competencies and self-directed learning also, individual and school factors determining these latent profiles. To this end, we used data from the "2022 Career Education Status Survey" of 1,363 students, including (804 male and 559 female students). Based on the latent profile modeling analysis, we identified three distinct latent classes of students' career development competencies and self-directed learning. These classes were named ‘low-level group,’ ‘intermediate-level group,’ and ‘high-level group.’ Second, multinominal logistic regression analysis revealed that gender, career activity participation satisfaction, need for career information, conversation with parents, career information awareness, desired career preparation and learning motivation were significant predictors of the latent profiles at the individual-level. Also, perception of the principal's perception of career education support, career teacher's job performance, and the number of career experience operations were derived as predictive factors that distinguish the potential class was significant predictors of the latent profiles at the school-level. Implications of this study and suggestions for follow-up research were discussed.
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks for Document Layout Analysis

Document AI aims to automatically analyze documents by leveraging natural language processing and computer vision techniques. One of the major tasks of Document AI is document layout analysis, which structures document pages by interpreting the content and spatial relationships of layout, image, and text. This task can be image-centric, wherein the aim is to identify and label various regions such as authors and paragraphs, or text-centric, where the focus is on classifying individual words in a document. Although there are increasingly sophisticated methods for improving layout analysis, doubts remain about the extent to which their findings can be generalized to a broader context. Specifically, prior work developed systems based on very different architectures, such as transformer-based, graph-based, and CNNs. However, no work has mentioned the effectiveness of these models in a comparative analysis. Moreover, while language-independent Document AI models capable of knowledge transfer have been developed, it remains to be investigated to what degree they can effectively transfer knowledge. In this study, we aim to fill these gaps by conducting a comparative evaluation of state-of-the-art models in document layout analysis and investigating the potential of cross-lingual layout analysis by utilizing machine translation techniques.
Bitcoin Narrative: A Consideration of the Development of an IC-PBL Course

This article critically examines the development process of the “Bitcoin Narrative” course, designed within the Industry-Coupled Project/Problem-Based Learning (IC-PBL) framework. It explores how humanities respond to the technological advancements of the Fourth Industrial Revolution. By shifting the perspective from a defensive stance to proactive engagement and practical application, the article highlights IC-PBL’s role in connecting humanities with the evolving technological landscape. The narrative of Bitcoin is used as a case study to investigate this intersection, delving into its philosophical, economic, and cultural origins. This approach also analyzes how Bitcoin’s development reflects broader trends in technology and society. The article addresses how Bitcoin’s initial anti-establishment ethos is gradually transforming towards institutionalization and regulatory acceptance. This shift underscores the evolving social context of technological innovations and the unpredictable nature of their impact on society. The “Bitcoin Narrative” course, as outlined in this article, aims to provide students with a critical understanding of Bitcoin’s influence on the humanities. It explores the ethical, social, and cultural dimensions of this digital currency. The course structure facilitates a comprehensive exploration of Bitcoin’s multifaceted implications, encouraging students to think critically about the intersection of technology and humanities.
ANALISIS HASIL PREDIKSI MAGNITUDO GEMPA DI WILAYAH KOTA PADANG MENGGUNAKAN TEKNIK RANDOM FOREST

The topic of earthquake prediction is an interesting one to research. As one of the natural disasters, earthquakes are included in natural disasters that can cause considerable damage. The damage caused not only affects the economy but also the life of an area. Several studies on earthquake prediction have been conducted so far, one of which is Machine Learning and using the Random forest method. The purpose of the research is to determine the analysis of earthquake magnitude prediction results in the Padang City area using machine learning techniques. The variables used in this study are time, latitude, longitude, and magnitude. Earthquake data is obtained from the United States Geological Survey (USGS) website. This research produces an RMSE value of 0.31758 and an MSE value of 0.10085
PEDAGOGICAL COMMUNITIES AS A RESOURCE FOR PROFESSIONAL DEVELOPMENT OF A MODERN TEACHER

The article examines the process of professional development as a factor in improving the quality of general education and reveals approaches to the professional development of teachers: professional development of teachers as a component of the development strategy of an educational institution; orientation of professional development to achieve high quality education; examination of the effectiveness of educational activities; organization of professional development as a collective activity based on solving urgent tasks of strategic development; continuous and long-term nature of professional development programs and the management of the professional development process. The content lines of professional development are determined. The role of professional pedagogical communities as an important innovative resource for the development of educational institutions is revealed, which allows them to improve their activities and solve urgent tasks by activating internal reserves. The professional pedagogical community is a self-learning organization, which is characterized by: managing the learning processes, including its training, retraining and advanced training, self-training of personnel to develop competencies and expand opportunities to master modern and promising technologies; investing resources in the process of training and self-training of employees, in improving its activities, content and technological equipment; maintaining the competitiveness of the organization by ensuring a high level of professionalism of employees as human capital and the main resource. For the professional pedagogical community, the status of a self-learning organization means the following: management of self-learning of teachers, self-learning as a way of innovative development, self-learning as improving the quality of education and competitiveness of the organization, team self-learning, stimulating self-learning.
Kesulitan Berbicara Siswa Sekolah Dasar dalam Pembelajaran Bahasa Inggris

The primary skill that shows language ability is the ability to speak English. On the other hand, most students have difficulty speaking. Therefore, this research aims to find the problems students experience when learning English, especially regarding speaking skills. English speaking skills are increasingly important in the current era of globalization. However, students in elementary school often need help with speaking English well. A crucial first step to overcoming English learning problems in elementary schools is understanding and analyzing the difficulties students face in speaking skills. Elementary school students in one of the Majalengka areas need help speaking English. One of them is the need to have greater self-confidence when speaking. Language and psychological factors can influence students' difficulties when speaking English. This research uses qualitative research using observations and interviews in elementary schools. Based on the results of the analysis, it can be concluded that five factors are responsible for students' difficulties in speaking English. They include lack of vocabulary, poor pronunciation and pronunciation, lack of friends or peers to talk to, lack of self-confidence, and class atmosphere. This research found that building students' self-confidence and providing organized activities is essential, especially in elementary schools.
THE ANTHROPOLOGICAL TURN IN K. D. USHINSKY’S EDUCATIONAL ANTHROPOLOGY AND THE PROBLEM OF “MAN-MASS”

The article examines the main pedagogical work of K. D. Ushinsky “Man as a Subject of Education: the Experience of Pedagogical Anthropology”. The experience of addressing the heritage of our outstanding teacher from the perspective of ontology and epistemology is demonstrated. A conclusion is made about the relevance of Ushinsky’s legacy in the light of the emergence of the phenomenon of “man-mass”, as well as his belonging to the “anthropological turn” occurring in Western philosophy in the 19th century.
THE SYSTEM FOR ASSESSING THE QUALITY OF EDUCATION AT A UNIVERSITY AS A BASIS EFFECTIVE MANAGEMENT

The Russian education system is actively beginning to use, in various types of monitoring of educational institutions, assessment of the quality of education based on self-examination reports, including information on the results of surveys of employers, teachers and students. The country’s universities are involved in this process in different ways: some have many years of experience in self-examination based on sociological research, others are just beginning this work as part of accreditation monitoring.
Penıngkatan Hasıl Belajar Model Project Based Learnıng Berbasıs Lapbook Kelas III SDN 1 Semplo

This research is based on a preliminary study obtained in the initial data on improving math learning outcomes which only reached 45%, so it is necessary to apply a project-based learning model based on lapbook media. This study aims to determine the improvement of student learning outcomes through a lapbook-based project-based learning model in third grade math lessons at SDN 1 Semplo. The type of research used is Classroom Action Research (PTK). The research subjects were third grade students of SD Negeri 1 Semplo with a total of 29 students. The data collection techniques used were observation, interviews, tests and documentation. The data analysis technique used was descriptive quantitative and qualitative. The project-based learning model which is a learning model that uses an activity, project as a medium that results in an increase in student learning outcomes, while the lapbook is a medium produced using project-based learning which can be a portfolio of student work and assignments. The increase in pre-cycle learning outcomes was 45% (13 students), increased in cycle I to 62% (18 students) and in cycle II increased to 86% (25 students). So, by applying the Lapbook-based Project Based Learning (PjBL) learning model can improve the learning outcomes of third grade Mathematics students of SDN 1 Semplo.
PENGEMBANGAN MEDIA PEMBELAJARAN ORASI PECAH BERBASIS FLIP PDF CORPORATE EDITION

The availability of an internet network in schools makes it possible for teachers to design web-based learning. This is in line with the development of the digital era which encourages classroom learning to transform from the use of printed books to digital books. Broken Oration Learning Media is a flip-based E-module, PDF corporate edition, which is a web-based learning media that is declared suitable and effective for use by experts. The Development Research method used is the 4-D method, namely define, design, develop and disseminate. The research trial subjects were 20 class V students of SD Negeri 1 Wates. The data collection techniques used are expert assessments, questionnaires and learning outcomes tests. The data analysis techniques used are feasibility tests based on expert assessments, effectiveness tests based on user responses, and learning outcomes. The results of the research show that the learning media for broken oration based on expert assessment received an average score of 4.6, which means that the learning media for broken oration is feasible and effective to use. Meanwhile, the results of data analysis from testing the effectiveness of e-module media based on student responses obtained a score of 91.80% who responded positively, and student learning results obtained a classical completeness score of 85.18%.
Cryptocurrency Conversion – the Problem of Mining Regulation

Today, the industry, referred to as «mining», is rapidly gaining momentum. Cryptocurrency mining is the extraction of digital assets by performing computational work on specialized equipment using special software. On the territory of the Republic of Kazakhstan, this industry has a great impact on the energy industry, since the operation of the above-mentioned equipment requires a huge amount of electricity to carry out the necessary calculations. Cryptocurrency mining activity is quite profitable at the moments of the peak value of cryptocurrencies. In Kazakhstan, cryptocurrencies are recognized as digital assets and are divided into secured and unsecured. Cryptocurrencies, as well as digital assets, are not suitable as a means of payment according to the laws of the Republic of Kazakhstan. Consequently, the collection of taxes from entities engaged in activities related to cryptocurrencies is a rather complex process. This work was written in order to solve the problem of converting cryptocurrencies into the state currency, to open up the possibility of building a centralized system for monitoring cryptocurrency mining within the state in the future, the article describes the structure of exchanges and platforms that enable cryptocurrency owners to exchange cryptocurrencies for traditional types of currencies
Research on Quality Evaluation of Strong Motion Data Based on Quantitative Indicator and Graded Evaluation Strategy

The motion observation data quality affects the reliability of results of disaster prevention, intensity rapid reporting, and early warning of earthquakes. Therefore, it is significant to evaluate the observation data quality. Based on the quality control over the strong motion observation data, we used the strong motion observation data for the qualitative and quantitative evaluations. The characteristics of the data were used to predict triggered and real-time transmission and determine evaluation indicators. Based on statistical analysis and simulations, a method to assess each evaluation indicator was developed. The graded evaluation and related standards were also established. The developed data quality evaluation method provides research ideas and technical support for the evaluation and control of strong motion observation data.
Studi Tingkat Motivasi Belajar Siswa Berdasarkan Hubungan Ibu dan Anak Kelas 5 di SD N 02 Dagen

Student learning motivation plays an important role as a driving force in achieving learning goals. In the learning process, it is important to achieve a balance between the level of student learning motivation and the learning objectives. Each student has a different level of motivation to learn, this is influenced by several factors, one of which is family environmental factors. The purpose of this research was to determine the level of student motivation to learn based on the relationship between mothers and children in grade 5 at SDN 02 Dagen. The relationship studied is the close relationship between mother and child. This research uses a mixed method approach which combines elements of qualitative and quantitative methods. The research subjects were students in class 5 at SDN 02 Dagen and had been selected based on the criteria the researchers needed. The results of the research show that there is an influence of the closeness of mother and child on the level of children's learning motivation. Parents and teachers need to pay attention to and increase children's learning motivation at school, and the role of parents at home must also be carried out well