Reviewerly/exHarmony huggingface dataset:
LEVEL OF EFFECTIVENESS OF LAND TRANSPORTATION OFFICE PORTAL TO THE USERS AND PERSONNEL IN LAGUNA LICENSING CENTER

The purpose of the study is to determine the level of effectiveness of Land Transportation Office Portal to the users and personnel in Land Transportation Office Laguna Licensing Center. Particularly, it aims to identify the demographic profile of the users and personnel in terms of age and ICT competence, determine the perceived level of effectiveness of Land Transportation Office portal among the users and personnel in terms of information quality, design and functions, reliability, security and privacy protection, system responsiveness, perceived usefulness, user satisfaction, and continuance intention. This research study utilized a descriptive – survey design. Total enumeration was applied to the personnel, while simple random sampling was used for the users. A self-constructed questionnaire was used as the research instrument, and the data collected was analyzed through simple linear regression analysis, frequency and percentage distribution, weighted mean, independent Ttest, and one-way analysis of variance. Based on the findings, the majority of respondents were between the ages of 16 and 25, and the majority had intermediate ICT competence. The study concluded that the LTO portal was highly effective in terms of information quality, design and functions, reliability, security and privacy protection, system responsiveness, perceived usefulness, user satisfaction, and continuance intention. However, there were significant differences in perceptions based on age and ICT competence, suggesting the need for targeted improvements in these areas. Establishment of a monitoring tool for the L TO portal is recommended for its continuous enhancement.
Problems of modern publishing: design, font and layout

The rapid development of Information Technology and computer technology has led to a huge change in the publishing industry. Today’s publishing industry has moved from a former mold machine to a modern automated system. In the last 10-15 years, domestic publishing houses have begun to use new computer technologies and search for them. The very fact that printed graphics began to be included in educational processes as part of computer technology means that society paid special attention to science and education, and from it the art of writing.In response to various historical and social changes, printed graphics have taken over the circle, adapting to various writing options and relying on the phonemic principle, from letter spelling to expression. The book’s decoration is determined by its internal content. However, because the first book’s cover catches the eye, the design of the book cannot be as important as the internal content. Despite extensive research in the field of domestic design, there are almost no works related to book design and font. This fact adds to the study’s importance. For a deeper disclosure of the topic, a qualitative research method was used. A qualitative comparative analysis of their products was carried out, taking as the object of study three printed works actively operating in the publishing market. As a result of this study, the main elements of book design were created ‒ by use of drawing and colors, and their dependence on the style of the shape properties of the main graphic elements. Using computer graphic methods, new examples of the use of drawing and colors in book design were demonstrated. In book design, there was a need not only to have professional skills and knowledge of the publishing industry, but also to master the psychology of color and design by a specialist.
Hate speech and legal regulation in digital communication

The article discusses the main characteristics of digital communication in the modern world and Kazakh society, as well as new transformations and forms of transmission of messages transmitted through digital communication.The digital media age makes any online chat or media product accessible with a single touch of the screen but makes it possible to share them without thinking about the consequences. In this regard, the article highlights the main advantages and harmfulness of intensifying and changing the exchange of information using modern technical means.As the number of media products on the Internet grows, so does the spread of hate speech. In this article, we identify and explore various ways to identify media products filled with hate speech on social networks. The problems and limitations we face in this direction also be mentioned. We also discuss the legal and practical aspects of resolving this issue.
HIGHLIGHTING THE USE OF END-OF-LIFE TIRE (ELT) AS A RENEWABLE MATERIAL AND THE POTENTIAL APPLICATIONS IN INDONESIA

The development of vehicles impacts the increased waste of used tires produced. One of The flooding of June 18, 2019 caused damage to the check dam structure on the Toili River branch, causing flooding during the rainy season and water being unable to flow into the Mansahang River which irrigates the Toili irrigation network and the Moilong irrigation network during the dry season. The purpose of this study is to design a bifurcation structure by modeling river hydraulics so that water can be supplied to the Moilong Irrigation Area and Toili Irrigation Area and to provide a sense of security from the threat of water damage. The data needed are: hydroclimatological data, watersheds, and river situation maps. Based on the results of the HEC-RAS analysis and simulation, the bifurcation structure of alternative-2 was chosen. This condition is very satisfying, where during floods the water can flow proportionally to the river branches and during the dry season the water needs are met.
Ontheissueofdefiningfunctionalcharacteristicsofethnicmediainthecontextofmigration

This article explores the potential of ethnic media in organizing cross-cultural dialogue andfosteringsocietalcohesion,aswellashighlightingtheconceptsofmigration.Thepurposeofthisstudyistostudythephenomenonofethnicmediathatarereachinganewlevelofactualizationinmoderngeopolitical conditions, as well as to consider the factors of thematic intersection of information spaces of diaspora andmigrant media. An attempt has been made to describe the functional features of these platforms in therepresentation of ethno-cultural diversity in the Republic of Kazakhstan. Various interpretations of theconcept of ethnic media, specifically as media diasporas, migrant communities, various ethnic groups, andminoritypopulations,havebeensubjecttoscholarlyinvestigation.In addition, this article presents a comparative analysis of the news agenda between ethnic journalismand the mainstream media industry. It also highlights the elements of ethnographic research, culturalanthropology, and ethnic journalism. Based on the content analysis of ethnic media in Kazakhstan, theauthors observe a low level of informational and adaptive support for migrants. They note the absence ofaneditorialconceptandlackofjournalistengagementinthisparticularsubjectmatter.
RESEARCH ON BLOCKCHAIN TECHNOLOGY FOR MICROCREDIT AND FINANCING IN THE EDUCATIONAL SECTOR

The article discusses how the current microcredit system has its limitations and risks. Classically structured financial institutions set high interest rates and require a large number of documents to obtain a loan. In addition, there is the problem of controlling the use of funds provided as part of a microloan. Blockchain technology can provide a transparent and automated system that will allow students and young researchers to receive microloans on affordable terms. The aim of our work is to study blockchain technology for microcredit in the educational sphere by writing and implementing the program code of the program "Smart contract for microcredit on the Ethereum platform". Methods. The study used data from various sources, including scientific literature, statistical data, and information from websites specializing in blockchain technologies and microcredit. A comparative analysis of these data was conducted to determine the potential advantages and disadvantages of using blockchain in microcredit. Results. The research of our program code has shown that the use of blockchain can have significant advantages for improving the microcredit process in education. In particular, blockchain can provide a more transparent and secure lending process, as well as reduce the risk of fraud. Discussion. Based on the results obtained, the advantages of blockchain technology for microcredit in the education sector are discussed. A number of limitations and issues that should be addressed when considering the application of this technology are discussed. The conclusions show that while there are some challenges associated with using blockchain for microcredit in education, its advantages as an innovative product to address these challenges are clear. Additional research in this subject area may help to unlock the full potential of this approach.
APPLICATION OF COGNITIVE GRAPHICS METHODS IN THE FORMATION OF A STRATIFIED TERRITORIAL SAMPLING

The paper discusses the approaches to constructing a model of a nationwide territorial sampling based on information technology, in particular, using cognitive graphics methods which improve the selection process of territorial units, a visual grouping of districts by population, types of geographical zones, etc. The article describes examples of working with specialized software designed to solve some of the applied problems of forming the office part of the sample and methods of using ready-made solutions in standard applications that can, by visualization, turn the data set into information suitable for further processing in order to generate new knowledge.
Development of Semantic-Web Based Forest Resources Database

There are some forest resources (such as Trees, Shrubs, and Water bodies) in the university main campus. These resources are used for various purposes ranging from teaching, researches medicinal and domestic purposes and among many others. However, there is no record of official documentation on them. Thus, this study aims to provide a semantic-web based database system for efficient management of forest resources. We adopted the W3C semantic web framework in the design and development of this work. Specifically, we focused on the layers 1 to 5 of the framework. The layers include; Resource Description Framework (RDF), Web Ontology Language (OWL), Extensible Markup Language (XML), User Interface). The Uschold’s and Kings’ method was applied in creating the ontology. The implementation process was carried out by populating the database with the Taxonomies of the Trees on University of Energy and Natural Resources (U.E.N.R) campus by means of creating an Ontology File using protégé. Some of the basic information that we provided by the proposed semantic web database include trees, their scientific name, geographical coordinate, physical location, medicinal value and other important data. We performed alpha test by involving six senior forest science students who used the application, evaluated it and came out with a good result which is shown in the chapter four of this documentation.
Experimenting FedML and NVFLARE for Federated Tumor Segmentation Challenge

It has been well accepted that artificial intelligence, machine learning, and deep learning (AI/ML/DL) techniques are on the path to making a revolutionary impact on health care. However, applying AI/MD/DL for clinical use is still challenging for reasons such as the concerns of security and privacy for sharing patient data among medical institutions, and medical data being heterogeneous. To solve those challenges, Federated learning (FL), which enables distributed training of DL networks without sharing data, could be a feasible solution as shown by the successful demonstration of FL in computer vision tasks. In the medical domain, Federated Tumor Segmentation(FeTS) challenge is the first challenge to address image segmentation using federated learning. In this paper, we evaluated and compared two FL frameworks, FedML and NVIDIA FLARE (NVFLARE), for FeTS 2022 challenge dataset in both distributed and centralized training methods. Using UNet as a baseline network, both FedML and NVFLARE are able to train the UNet models, and the accuracies of the two models are close to the accuracy of the model trained from centralized training. Models trained by NVFLARE performs a little better than models trained by FedML.
Robust and Reliable Liveness Detection Models for Facial Recognition Systems

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model’s accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system.
Graph Open-Set Recognition via Entropy Message Passing

Graph Neural Networks (GNNs) have achieved great success in semi-supervised node classification. These methods usually assume the closed-set setting and classify unlabeled nodes to known classes. However, in reality, there exits some unknown classes due to biased-sampling, distribution shifts, anomaly, etc. Therefore, it is important to identify unknown classes while classifying known classes, which is defined as Graph Open-Set Recognition (GOSR). To alleviate this problem, we propose Entropy Message Passing (EMP) for GOSR, which takes into account the graph structure information when identifying unknown classes and automatically determines the discrimination threshold. To be specific, we calculate the likelihood of a node belonging to unknown class through entropy propagation. Then, we transform the threshold selection into entropy clustering to identify the unknown class nodes. Finally, we classify the remaining nodes. Experimental evaluations on six benchmark graph datasets demonstrate that our method outperforms state-of-the-art baseline methods in unknown class detection and graph open-set recognition tasks. Especially in unknown class detection, our method has achieved a significant reduction in FPR@95 ranging from 12.62% to 55.88%.
Designs and Implements the electronic Signature System of Open Fixed-layout Document (OFD) format documents

With the rapid development of document security and authenticity requirements in office automation, the demand for electronic signature is more strict, and it needs to realize the functions of multi-party countersignature, signature verifiability, authentication, non-repudiation and so on. Therefore, the electronic signature system of a new generation of electronic signature products based on OFD format files comes into being. This paper focuses on realizing the function of OFD format documents in the electronic signature module, adopts MVC design pattern, uses the state secret algorithm, builds the electronic signature system architecture of OFD format files based on Spring MVC framework, and completes the key query of OFD format files, the technical difficulties such as the display of the seal and slit, and supports single-page seal and multi-page seal. A variety of signature rules, such as seal, keyword seal, transparent seal and so on, support the nested call of signature rules, flexible design and convenient integration, which is of great significance for the promotion and application of national format document OFD and the realization of the core technology of domestic electronic document system localization.
Impact of QAED Punjab Promotion-linked Training on Developing Teachers' Competencies for School Educational Leaders in Punjab: A Meta-Analytic Study

Training has a significant impact on teachers’ development. This study aims for a meta- analysis on the impact of the Quaid-e-Azam Academy for Educational Development (QAED)/DSD Punjab's promotion-linked training program enhancement of teachers' competencies, specifically focusing on their role as educational leaders in schools across Punjab, especially school leaders. The data sources in this meta-analysis research are nine national research papers that have been published from 2018 to 2023. The method of selecting data sources is the PRISMA method. The data sources are Google Scholar, Eric, and Proquest. Data analysis is a quantitative and qualitative analysis that involves calculating the effect size value, standard error, and average value (mean) with the help of the application. The study indicates that the research explores various dimensions of teachers' competencies, such as pedagogical skills, classroom management, communication, curriculum development, and innovative teaching methods.
UPAYA MENINGKATKAN PERHATIAN BELAJAR MELALUI MEDIA GAMBAR DENGAN MODEL PEMBELAJARAN DIRECT INSTRUCTION PADA SISWA KELAS II UPT SD NEGERI 11 PASUI

Efforts to Increase Learning Attention Through Media Pictures With Direct Instruction Learning Models in Grade II Students. This study aims to increase students' learning attention by using a learning model direct intruction. This type of research is classroom action research. This classroom action research uses a cycle model of planning, action, observation, and reflection. The subjects of this study were class II UPT SD Negeri 11 Pasui students. The number of students in the class were 13 students who consists of 3 male students and 10 female students. The results of the study show that the results of the evaluation of improvements in learning Indonesian appear to be quite good. Mastery of learning material increased from an average of 70 in the initial data, to 78 in the first cycle. Meanwhile, the level of learning completeness increased from only 36% in the initial data to 60% in the first cycle, there was an increase of 30% in the first cycle. cycle II there has been another increase to 81. Thus it can be seen that from the initial conditions to cycle I there has been an increase in grades meaning there has been an increase in learning. Then when viewed from cycle I to cycle II there is also an increase in the average value and the lowest score obtained by students. The results of this study can be concluded that using media images with the Direct Instruction learning model can increase students' attention in the learning process.
Requirement Identification for Blockchain-based Secure Transmission of Oil and Gas

Currently, blockchain is using different areas of life such as finance, education, health, government sector, banking, property chain, etc. As digital technology advances, more and more companies are integrating blockchain to improve security, privacy and data transparency for both small businesses and large enterprises. Hence, there is a need to define a software requirement clearly, and precisely so that developer develops the application efficiently. This paper discusses the requirement analysis of one of the blockchain-based applications for the secure transmission of Oil and Gas.
Comparing the effectiveness of cognitive behavioral game therapy and education based on acceptance and commitment on cognitive flexibility and academic resilience in students with specific learning disorders.

Background and Aim: Studies show the effect of learning disorder on cognitive flexibility and academic resilience of students. The purpose of this research was to determine the effectiveness of cognitive behavioral game therapy on cognitive flexibility and academic resilience of students with learning disabilities. Research method: In this research, a semi-experimental method and a pre-test and post-test design with a control group were used. The statistical population of this research included female elementary school students in six districts of Tehran, who had learning disabilities and were referred to learning disabilities rehabilitation centers. 60 students were selected by purposeful sampling and randomly without replacement in two groups (20 people in each group). Data were analyzed using covariance test at 5% level using SPSS-23 tool. Results: The results of the research showed that the adjusted average of the cognitive behavioral therapy game group was higher than the acceptance and commitment based training group, as a result, the cognitive behavioral therapy game has a greater effect on increasing the cognitive flexibility and resilience of students with specific learning disorders. It is committed to education based on acceptance and commitment. Conclusion: According to the results, this study shows that cognitive-behavioral game therapy and education based on acceptance and commitment have a positive effect on cognitive flexibility and academic resilience in students with specific learning disorders.
Minimizing the weight uncertainty in Neural Networks Using Bayesian Optimization

Here is a draft abstract comparing convolutional neural networks (CNNs) and Bayesian neural networks (BNNs) for modeling uncertainty in neural networks: Neural networks have become a popular and powerful tool for many machine learning tasks. However, standard neural networks tend to produce overconfident predictions and do not properly model uncertainty. This is concerning in applications such as medical diagnosis or autonomous driving where assessing model confidence is crucial. Two approaches to address this limitation are Bayesian neural networks (BNNs) and ensemble methods such as convolutional neural networks (CNNs). BNNs place prior probability distributions over model weights and use Bayes’ rule for inference. This allows BNNs to represent both epistemic uncertainty and aleatoric noise. Meanwhile, CNN ensembles train multiple models on different subsets of the data and aggregate their predictions. This captures more realistic variance across the ensemble. In this work, we evaluate and compare these two approaches for modeling uncertainty on image classification tasks. Our experiments show that BNNs can better capture epistemic uncertainty due to limited data; Predictions are aggregated across the ensemble, capturing variance due to data subsampling. While CNN ensembles do not explicitly model epistemic uncertainty, the variance between models increases with greater model capacity and smaller datasets, partially capturing this effect. Ensembles are computationally expensive, requiring training multiple models.