Reviewerly/exHarmony huggingface dataset:
Training as a method of preparing future specialists in special education for partnership in professional activities

The article describes the results of the study of the effectiveness of training as a method of forming the ability to partnership in the professional activities of future professionals in the specialty 016 Special Education. Normative documents in the field of higher education testify to the importance of forming the ability to partnership of future specialists of special education. It is proved that the choice of training method for the formation of professional competencies is due to its clear practical orientation and the ability to acquire not only professional skills and abilities, but also to improve social ones. It is established that the majority of students believe that the discipline "Training of collective interaction" contributes to the formation of the ability to partnership in the professional activities of future specialists in special education. A significant part of students noted that after studying the discipline "Training of collective interaction" acquired the following skills and abilities: to make an adequate choice of forms and methods of forming collective interaction, conflict resolution; to form a team of pupils of special and inclusive institutions; ability to apply knowledge in practical situations. There is a decrease in acquired skills among students from IV to I course, which may be due to forced transition to distance learning (due to COVID-19 and martial law), because the study of the discipline "Training of collective interaction" requires classes in the classroom. It is established that a significant part of students consider the discipline "Collective Interaction Training" effective in training future specialists in special education and are ready to recommend it for study to other applicants for higher education among elective disciplines. A significant part of students expressed a desire to study various training disciplines to improve their professional competence. Experimental study of the effectiveness of training as a method of forming the ability to partner in professional activities has shown its feasibility and effectiveness.
The Effect of Knowledge Management on Organizational Performance with the Mediating Role of Differentiation Strategy in Agricultural Companies of Science and Technology Park

The Effect of Knowledge Management on Organizational Performance with the Mediating Role of Differentiation Strategy in Agricultural Companies of Science and Technology Park
Tele-Education in Family and Patients in HIV/AIDS Management During Treatment

This study aims to analyze how the influence of vidio learning media on student competency achievement. The method used in this study was carried out by taking data from PubMed, ProQuest, Google Scholar databases, which were found in 2015-2021. The results showed that some of the learning media used by students were Vidio. In conclusion, Vidio as a learning medium is very influential in increasing knowledge, helping to get information about assignments, knowing the development of information, and can be used as material to deepen the material.&#x0D; &#x0D; Keywords: Students, Learning Media, Vidio (Learning Media, Vidio, Student)
Formation of digital competence in higher education students as a basis for the transformation of education of the future

The education informatization together with the rapid spread of computer technology actualizes the need for thorough information training of future teachers. The purpose of the study is to analyze the applicants for higher education digital competence based on the features of modern education. In the work were used general scientific methods: analysis, synthesis, deduction. By means of prediction method, further opportunities for the development and improvement of the digital competence of students are characterized. The study contains a systematic literature review on the development of digital competence in higher education students, published over the past decade. Based on a full-text analysis of 20 articles published in known databases, the study identifies the need for developing digital literacy skills in undergraduates. The results define the role and importance of digital competence for future specialists’ formation, outline the structure of digital literacy, highlight the main tasks of digital competence: the creation of digital content, digital culture of communication and cooperation, formation of information control and interpretation skills, etc. Although the digitalization of education is active in Ukraine, but the study highlights the possibilities of its development: improving the legislative framework, state support for the implementation of digital products, the development of digital educational content, the development of innovative material and technical base, etc. To conclude, the effectiveness of the development of digitalization of education is provided by the continuity, consistency, and coherence of preparation for the digital transformation. At the same time, digital competence is a necessary condition for the formation of the teacher of the future: they should be able to analyze information, work in network communities to design and form a digital educational environment in order to form a new generation of progressive teachers.
Aplikasi Penilaian Kinerja Dosen pada Proses Belajar Mengajar Berbasis Web: Studi Pada STTI NIIT

This article writes about making a lecturer performance questionnaire application which is made to make it easier to convey the aspirations of students to the maximum with access that can be done anywhere. Apart from being used as a means of aspiration for students, this application is also useful for monitoring lecturer performance, lecture material, and managing relevant data for admins. This application consists of one page for the user and one page for the admin. The user page only has questionnaire questions, while the admin page can manipulate data such as changing, adding, and deleting questions. On the admin page there is also a menu to view graphs and questionnaire results from users. The results of this application are very useful for improving the performance of the teaching and learning process at the STTI NIIT, Jakarta.
The Role of Literature Teaching in Improving Students' Language Skills

Students' views on language learning are an important foundation in formulating language learning plans and processes. This study aims to look at students' views about the benefits of learning literature in language learning at the high school level. This study also seeks to reveal differences in the views of students from several schools. Researchers use a comprehensive approach to analyse students' views about the benefits of learning literature in learning. Researchers used a survey research method involving an open-ended questionnaire to see students' views on the benefits of learning literature in language learning. The research participants were 555 high school level students from fifteen schools. The research findings show that most students provide a positive view, that is, learning literature in learning Indonesian really supports the linguistic aspects of language learning. Generally, students' views of the benefits of literature are based on the teaching approach used sequentially from the largest to the smallest percentage, namely the language approach, the context approach, followed by the reader's approach and the text approach. The most proportionate combination of approaches is the language approach with context. Some differences from the research sample regarding the benefits of learning in aspects of supporting language skills are caused by differences in the style of approach or pedagogies used by teachers. The results implied that teachers can see and consider students' perspectives or views regarding the benefits of learning literature which are then used to support language learning.
Solving Turkish math word problems by sequence-to-sequence encoder-decoder models

Solving math word problems (MWP) is a challenging task due to the semantic gap between natural language texts and mathematical equations.The main purpose of the task is to take a written math problem as input and produce a proper equation as output for solving that problem.This paper describes a sequence-to-sequence (seq2seq) neural model for automatically solving Turkish MWPs based on their semantic meanings in the text.It comprises a bidirectional encoder to comprehend the semantics of the problem by encoding the input sequence and a decoder with attention to extract the equation by tracking the semantic meanings of the output symbols.We investigate the success of several embedding types, pretrained language models, and neural models.Our research is novel in the sense that there exist no studies in Turkish on this natural language processing task that utilizes pretrained language models and neural models.There is also no Turkish dataset that can be used to train the neural models for the MWP task.As the first large-scale Turkish MWP dataset, we translated the well-known English MWP datasets into Turkish using a machine translation system.Although Turkish is an agglutinative and grammatically challenging language, the proposed models achieve around 72% accuracy on the dataset compiled from three English datasets.
PREDICTION OF HEART DISEASE USING MACHINE LEARNING ALGORITHMS AND NEURAL NETWORK

In this paper we carried out research on heart disease from data analytics point of view. Prediction of heart disease is a very recanted as the data is becoming available. Other researchers have approached it with deferent techniques and methods. We used data analytics to detect and predict disease's patients. Starting with a pre-processing phase, where we selected the most relevant features by the correlation matrix, then we applied three data analytics techniques (neural networks, SVM and KNN) on data sets of different sizes, in order to study the accuracy and stability of each of them. Found neural networks are easier to conure and obtain much good results (accuracy of 93%).
Time-Bound Investigational Text Analysis of Predatory E-mails Using Pareto Principle

Predatory act in the scientific field is a progressively growing problem that impacts the integrity of the scientific community.While there are several guidelines available to avoid them, young researchers and inexperienced scientists may not come across them unless it is too late.This might be a challenge, especially for those working in the non-academic sector where a lack of clear guide or reference is not uncommon.The objective of this work is to provide a simple and straightforward guide for recognizing predatory patterns through simple and commercial tools in the analysis of the messages which are used as traps to bring the victims into the net of the intruders ruining the efforts of the researchers and destroying their reputation, in addition to the adverse effects on the progression of their career.The E-mail of an industrial researcher was screened for spam messages for those with scientific nature and then they were isolated in an Excel sheet for processing using the MeaningCloud Add-in platform for text analysis followed by further examination using Pareto charting for prioritization of the contributors using Minitab and Excel.Predatory e-mail senders showed an encouraging pattern to the victims with a systematized insistent attack on the inbox.They hide under the general scope of science, even claiming specificity and professionality.Positive sentiment tendency is predominant in the predatory texts to drag the victim into involvement actions with the fraudulent.Phishers tend to use a bunch of specific keywords to drag their prey to their traps with apparently unprofessional messages that could be sighted through text messages.
Penerapan Model Pembelajaran Project Based Learning Untuk Meningkatkan Hasil Belajar Siswa Siswa Kelas V SDN 119 Sarira

This study aims to improve learning outcomes through the application of a project based learning model for class V SDN 119 Sarira. The approach used in the research is a qualitative approach and classroom action research (CAR) which consists of 4 stages, namely planning, implementation, observation and reflection. Research subject are the fifth grade students of SDN 119 Sarira as many as 30 people consisting of 12 boys and 18 girls. The research instrument used was the teacher’s observation sheet and the student observation sheet. II increased with a completeness level of 70% with a good category. Based on the results of the researches, it can be concluded that the application of the project based learning model can improve the learning outcomes of fifth grade students at SDN 119 Sarira.
One-year Dynamic Study of the Use of Solar Water Heater for Domestic Consumption in Egypt: An Energy Assessment

The use of fossil fuels leads to greenhouse gas emissions, global warming, and secondary consequences such as desertification and winds in the Middle East and Africa, including Egypt. The use of renewable energy is the most appropriate solution to prevent the emission of polluting gases. Egypt is one of the best places to use solar water heating systems, located in the solar belt. In this paper, for the first time, the best place to use solar water heater (SWH) systems is examined using TSOL 2018 R(1) software and climate data for residential apartments in 35 stations in Egypt. The results showed that Sharm el sheikh station with supplies 96.8% of its total heat needs is the most suitable station for using solar water heating systems. According to the studies performed, using solar water heaters in the studied stations generated good energy savings annually (production of 134.5 GWh of solar heat). Also, greenhouse gas emissions were significantly reduced (preventing the emission of about 39.2 tons of CO2 pollutants per year) and as a result, the government should turn to the use of clean and renewable energy.
ALGORITHM VISUALIZER

Everybody has a different way of learning new information, this is called your learning style. Our learning style is how we best learn information. One of the most popular learning styles of 21st century is visual learning. “Algorithm Visualizer” is an online visual learning tool which acts as a catalyst in learning complex computer science algorithms. It provides an interactive experience for a learner to visualize the end-to-end implementation of an algorithm. The application contributes in solving the burden of learning two of the most popularly known algorithms, searching and sorting. Key Words: Algorithm visualizer, Sorting, Searching, Algorithm.
DaFKD: Domain-aware Federated Knowledge Distillation

Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft predictions for some given input distillation sample, which does not take the diversity across all local models into account, thus leading to degraded performance of the aggregated model, especially when some local models learn little knowledge about the sample. In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models. Specifically, we employ a domain discriminator for each client, which is trained to identify the correlation factor between the sample and the corresponding domain. Then, to facilitate the training of the domain discriminator while saving communication costs, we propose sharing its partial parameters with the classification model. Extensive experiments on various datasets and settings show that the proposed method can improve the model accuracy by up to $6.02\%$ compared to state-of-the-art baselines.
Lightweight Human Pose Estimation Based on Self-Attention Mechanism

To tackle the issues of numerous parameters, high computational complexity, and extended detection time prevalent in current human pose estimation network models, we have incorporated an hourglass structure to create a lightweight single-path network model, which has fewer parameters and a shorter computation time. To ensure model accuracy, we have implemented a window self-attention mechanism with a reduced parameter count. Additionally, we have redesigned this self-attention module to effectively extract local and global information, thereby enriching the feature information learned by the model. This module merges with the inverted residual network architecture, creating a separate module of WGNet. Finally, WGNet can be flexibly embedded into different stages of the model. Training and validation on COCO and MPII datasets demonstrate that this model reduces the number of parameters by 25%, computational complexity by 41%, and inference time by nearly two times, compared to Hrformer, which also utilizes the windowed self-attention mechanism, at the cost of only 3.5% accuracy.
A Survey of Blockchain-Based Smart Contract Application Testing Framework in the Energy Industry

Blockchain is the fundamental component of smart contract applications. Testing technology plays a special and irreplaceable role in the development of smart contract applications in blockchain-based applications, especially in distributed renewable energy transaction scenarios in the energy industry. In practice, the formulation of blockchain technology as a standard infrastructure is an essential means to improve the reliability of blockchain-based applications in the energy industry. However, the quality of the organization-level blockchain still encounters many challenges, such as password attacks, and double spending attacks, which attract much attention from both research and academic area. Much research has focused on quality improvement through testing to fulfill the requirement toward functional, performance, and security requirements of the industry. However, the framework to accomplish the specific testing task was not present comprehensively yet. In This paper, an investigation was given on the supervision and testing of blockchain-based applications in the energy industry after its operation online. The existing testing indicator, model, and application scenario combined with the practice in the state grid industry were illustrated, in which the process, models, and methods were shown, and also suggestions were given on promoting the blockchain-based smart contract testing evaluation in the energy industry.
Application of Laser Ranging in Path Planning of Mobile Medical Robot

Compared with traditional industrial robots, mobile robots with autonomous sensing, decision-making and execution functions have broad application prospects. With the continuous development of computer science and sensor technology, the research of mobile medical robot is also developing in the direction of intelligence and autonomy. The mobile medical robot senses the environmental information and its own state through sensors, and realizes the autonomous movement facing the target in the environment with obstacles, thus completing the medical functions specified by the designer. When applied to some practical scenes, the environment faced by mobile medical robots is often full of complexity and unknowns. Accurate 3D scene model is beneficial for robots to obtain accurate pose estimation by data matching and other methods, and can generate collision-free navigation paths based on environmental information. The mobile medical robot can move autonomously safely and effectively only if it can accurately and automatically position itself, the obstacle information in its working environment and its movement state.
Explainable Itemset Utility Maximization with Fuzzy Set

&lt;&gt;Recently, fuzzy utility pattern mining has received much attention for its practicality and comprehensibility. It aims to discover high fuzzy utility itemsets (HFUIs) by considering not only utility but also linguistic factors. Among existing algorithms, experiments showed that fuzzy-list-based algorithms are effective and efficient. However, a significant disadvantage of fuzzy-list-based algorithms is that constructing and maintaining fuzzy-lists is time-consuming and memory-overhead. To address this issue, a novel algorithm named explainable Itemset Utility Maximization with Fuzzy Set (FS-IUM) is proposed in this paper. The traditional fuzzy-list structure is replaced by a better structure (i.e., fuzzy-list buffer), which speeds up the mining process and reduces memory consumption. Compared with fuzzy-list structure, the fuzzy-list buffer structure and its auxiliary structure help the algorithm locate the fuzzy-list quickly and thus reduce the runtime. Moreover, with an efficient fuzzy-list buffer construction method, the algorithm reduces the cost of candidate storage. Furthermore, with several efficient strategies, the proposed algorithm can prune numerous useless patterns in advance and thus considerably reduces the runtime usage. Finally, extensive experiments on various datasets were conducted to compare the performance of FS-IUM with some state-of-the-art algorithms. The experimental results reveal that the proposed fuzzy-list buffer-based algorithm highly outperforms the baselines in terms of runtime and memory consumption.&lt;&gt;
A New Remote Sensing Image Retrieval Method Based on CNN and YOLO

&lt;&gt;Retrieving remote sensing images plays a key role in RS fields, which activates researchers to design a highly effective extraction method of image high-level features. However, despite the advanced features of the image can be extracted by deep-learning method, features fail to cover the overall information due to its rich and complex background. Extract key regions from RS images, recede background interference and retrieval accuracy needs to be solved. Combined YOLOv5 target recognition algorithm with deep-learning method, this paper proposes a novel retrieval method based on target critical region detection. Firstly, the key retrieval regions have been identified. YOLOv5 target recognition algorithm has been used to identify the key regions of the image and served as the retrieval regions. Secondly, the retrieval characteristics are determined. Combining with the CNN model ResNet50, the retrieval features are extracted from the retrieval regions acquired in the previous step, in addition, PCA method has been used to reduce the dimension of the retrieval features. Finally, using weighted distance based on class probability to measure the similarity between a query and retrieve images. Experimental results show that the proposed method can extract better image retrieval features and improve the retrieval performance of RS image.&lt;&gt;
A Review of Traditional Dehazing Methods for Foggy Images

Photographs taken under foggy conditions are prone to low contrast, color deviation, and blurred local details, which are detrimental to advanced computer vision tasks such as image recognition, semantic segmentation, and image detection. Although deep learning techniques have made significant progress in image dehazing, the effectiveness and role of traditional image dehazing methods are still fundamental and have their unique advantages. Based on this, the principles and advantages of the existing advanced traditional methods are analyzed from image restoration and enhancement perspectives, and standard datasets are introduced. Finally, predictions for the future direction of the traditional image dehazing field are presented.