Reviewerly/exHarmony huggingface dataset:
Research on intelligent decision-making mechanism of the elderly care system based on fuzzy neural network

In order to solve the problem that the existing detection mechanism only using the fall sensors easily leads to the elderly care system receiving false fall help signals, an intelligent decision-making mechanism based on a fuzzy neural network is proposed. After receiving the fall help signal reported by the person label, the mechanism first reads the sensor data collected by the person label at that moment, then standardizes the sensor data, and finally inputs the standardized sensor data into the fuzzy neural network model to analyze and judge the authenticity of the fall help signal. The results show that the intelligent decision-making mechanism based on the fuzzy neural network can effectively eliminate the false fall help-seeking signal caused by the false judgment of the fall sensor, and the judgment accuracy of the fall help-seeking signal can reach 97. 5%.
Research on counting method of half-smooth tongue sole fry based on machine vision

In response to the problems of large errors and low efficiency in the current stage of manual completion of fry counting of half-smooth tongue sole, this paper proposes a counting method based on machine vision technology, which consists of a counting algorithm combining skeleton endpoints and crosspoints and a compensation algorithm based on this counting algorithm. First, the fry images are acquired and preprocessed to obtain the binarized images, all the connected domain parameters are acquired and the corresponding connected domain images are constructed, and the fry skeleton images are obtained using the skeleton thinning algorithm. Subsequently, the defects of the endpoint counting method were analyzed. The crosspoint counting method was proposed, and new rules for determining endpoints, skeleton points and crosspoints were established. The two counting methods were compared and analyzed under different fry distribution situations. Then, a compensation algorithm based on crosspoint counting is proposed. The process of obtaining the average standard area of fry and how to set the scale factor reasonably are presented. Finally, the fry images were divided into three groups, A, B and C, according to the degree of overlap and counted using two methods. The results showed that the accuracy of endpoint counting method was 79.97%, 64.06% and 46.28%, and the accuracy of crosspoint counting method was 98.49%, 98.65% and 98.93%, respectively.
Evaluation of the Hastings-Powell Chaotic System and its Implementation in Digital Image Encryption

In the current digital era, data security is increasingly vital due to the abundance of valuable information stored digitally. One promising approach in data security systems involves the utilization of chaotic systems, such as the Hastings-Powell chaotic system. In this research, the initial investigation focuses on analyzing the phase diagrams and bifurcation diagrams of the Hastings-Powell system. The chaotic nature of this system enables it to function as a random number generator, serving as a key in the XOR method to generate cipher data for encryption purposes. This paper extensively evaluates the effectiveness of the employed algorithm for image encryption by conducting various analyses, including histogram, correlation, entropy, sensitivity, and noise attack analysis. The outcomes of these analyses contribute to establishing the potential of the Hastings-Powell system as a technique for masking data during transmission. To validate the research findings, numerical simulations have been performed using Python.
Classification of Autism Using Feature Extraction Speed Up Robust Feature (SURF) with Boosting Algorithm

Autism is a complex disorder that affects an individual's neurobehavioral development. This disorder is characterized by restricted and repetitive communication patterns, difficulties in social interaction, and changes in sensory processing. This study aims to classify autistic and normal faces by using feature extraction using the SURF (Speeded Up Robust Features) method and the boosting algorithm. This study uses several variants of boosting algorithms, such as Adaboost, Gradient Boosting, and LightGBM. The dataset used in this study consisted of 100 samples of autistic faces obtained from Special Schools in Medan, as well as 100 samples of normal faces. Comparison of training data and data testing is 70%:30%. The results of this study indicate that the boosting algorithm provides good performance in classifying autistic and normal faces. Among the boosting algorithm variants used, Gradient Boosting achieved the highest accuracy of 91.67%, followed by LightGBM with 88.33% accuracy, and Adaboost with 81.67% accuracy. This finding demonstrates the effectiveness of the boosting algorithm in this classification task.
Multi-strategy fusion improved Archimedes optimization algorithm

To solve the problems of the AOA algorithm, such as the weak ability of global exploration and local development, low accuracy, and easily falling into local optimum, a multi- strategy fusion improved Archimedes optimization algorithm (MAOA) is proposed. First, two kinds of chaotic mapping and reverse learning are used to initialize the population, so that the population has a good initial solution. Secondly, the transfer operator is improved to make the conversion between global exploration and local development not monotonous and enhance the convergence ability. Finally, the adaptive Gaussian mutation strategy is introduced in the local development stage to jump out of the local optimum as much as possible and improve the convergence accuracy. The simulation experiment compares the MAOA algorithm with the standard AOA algorithm and other meta-heuristic algorithms under eight benchmark test functions. The experimental results show that the MAOA algorithm has good comprehensive performance in terms of solution accuracy and convergence speed.
Formation of Soft and Selfskills within the Framework of the Simulation Game "Primary Accreditation"

The article shows the advantage of learning at the process factory (primary accreditation) for the formation of soft and selfskills in comparison with traditional lectures.
Improved permanent URL schema for r-universe.dev

&lt;strong&gt;Pretty permanent URLs!&lt;/strong&gt; We have improved the URL schema for the r-universe.dev front-end to make it even easier to find and share a link to an R package, article, API, or other resource. The old URLs will now automatically be redirected to the new locations, so this should be a non-breaking change.
Research on the Teaching Management Path of Universities in the Background of Innovative Education

In the background of the new era, the in-depth application of advanced technology and the Internet has brought new development opportunities for the society, and at the same time made college students face new challenges and tests. Both the better development of individuals and the sustainable progress of society require continuous innovation and creativity, which is a new requirement of the times for education and individuals. As the basis for cultivating and exporting high-quality talents, universities should not only apply effective teaching methods and paths to improve the quality of professional teaching, but also focus on cultivating students' innovation consciousness and creative ability. Both professional teaching and innovation education need to start from the teaching management mode to create good conditions for the implementation of innovative education and professional teaching activities with good teaching management quality.
Detection of Mild Cognitive Impairment Using a Spiral Drawing Test

Information and communication technology-based monitoring services for the elderly can be improved by using a monitoring function that automatically detects cognitive decline. Therefore, we developed a system that provides a brief screening for mild cognitive impairment (MCI). This screening is performed by measuring the performance of two tasks completed simultaneously on a tablet device, such as drawing a spiral and counting color changes. First, we used this system to measure and analyze three groups: elderly persons suspected of having MCI, elderly persons with no cognitive impairment, and healthy young adults. We extracted evaluation parameters relevant for determining MCI through analysis. Furthermore, we developed a detection method using the evaluation parameters and performed an evaluation. Cross-validation revealed that the sensitivity and specificity of this method were 90.0% and 75.0%, respectively.
Spatial-temporal relationship between gold ore deposits and stratiform polymetallic deposits in blackshale sequences of the Olokit ore

A model is suggested for the spatial-temporal coexistence of stratiform polymetallic massive sulfide deposits and stratoid gold ore deposits, confined to the flyschoid blackshale sequences of the Olokit ore cluster. Two types of the settings (the remote and combined ones) were identified for these genetically different ore deposits of base and precious metals, formed within a single formation-rank stratigraphic level of sedimentation.
Некоторые методологические вопросы преподавания попджазового пения

The purpose of the article is to consider the methodological tasks of teaching the art of pop-jazz singing at the present stage, related to the performing technical apparatus of the vocalist, starting from the beginning stage and through the formation and development the student's voice. Tasks: methodological approaches based on pedagogical experience, which this article is devoted to, are presented. The author notes that students start classes at different ages, so professional education and teaching methods in music universities should contribute to this process. The task of the teacher is also noted - to be attentive and provide an individual approach to each student in accordance with his abilities, level of development, and of course, take into account style preferences - pop or jazz music.
Using a Convolutional Neural Network to Convert Black-and-White Photos into a Colorized Format in the Telegram Bot

Artificial neural networks will be used to solve our problem, since they are best suited for making predictions, due to their ability to detect non-obvious connections and the most general patterns of input and output data. A diagram of the architecture of the created neural network has been developed. Lab color coding method considered. The program is implemented in the high-level language Python. To create a neural network, an open software library for machine learning TensorFlow was used. The ReLU function (linear rectifier or semi-linear element) was chosen as the activation function for all layers, except for the output one. A telegram bot has been developed, the main purpose of which is to convert the sent black-and-white images into a color format. With the help of the developed software algorithm, it is also possible to solve the problem of low quality and low resolution of images by processing them with an online neural network. After the received images can be saved
An Android e-Learning Application to Support Academic Learning: Design, Development, and Implementation of a Case Study

After the pandemic, Indonesia became one of the countries that imposed a new normal for all levels of society with various sectors of life. The education sector is one of the areas most highlighted in the new normal change. Various policies and learning methods have been attempted to implement distance education. In the current new normal, distance learning is a beneficial method in implementing education in Indonesia. The delivery of learning materials is carried out by sending materials and meeting face-to-face online using video conferencing applications and materials provided on online storage services. Both methods are now available on separate platforms and have not been integrated into one platform, making it difficult for users. It has shortcomings in terms of optimization in providing learning services. In addition, the method used today still has several weaknesses, such as adjusting the learning schedule, understanding that is more difficult to accept because it is done online and also not yet integrated between face-to-face materials and media on a mobile device. This study aims to design an Android-based e-learning application that can assist students’ learning process and help distribute learning materials. The application is designed to meet the needs of students in understanding the material more optimally and can be done without being limited in time. The results of research that have been carried out on student satisfaction obtained a percentage level of 94% which stated satisfaction in the use of learning applications. Satisfaction with the help of the application says that e-learning applications can improve academic abilities and skills in the fields they study.
Education Quality Problems in Using Distance Learning Technologies

The article deals with the process in distance learning in the Republic of Kazakhstan, as one of the main parameters for introducing digital literacy development at the tertiary level. Buketov Karaganda University has been taken as the research site for data collection and analysis of quality distance learning. The authors have revealed the devices used by students in distance learning, the amount of available educational materials, the positive aspects of online teaching. At the same time, the paper aims at identifying the problems of distance learning in Higher Education Institutions (HEIs), electronic library resources. The analysis proved the effectiveness of distance learning at the university. It suggests some assumptions on the improvement of distance learning and education technology based on the survey results among students and teaching staff. It is believed that the development of distance education and their technologies will enable HEIs to meet students’ demands and rights to quality education. It will allow anyone to receive bachelor degree and professional engagement simultaneously
Challenges of Online Learning in New Strategic Trends in the Educational Space

An innovative approach to the study of strategic dynamics is presented, pedagogical conditions are studied that optimize the educational process, and shows the heterogeneity of didactic and educational strategies. In the context of the purpose and objectives of the study, the issues with the implementation of innovative activities in line with new strategic trends are considered. The acmeological-synergetic approach is used as an internal source of the axiological core formation, taking into account psychological and didactic barriers. Characteristic strategies are singled out from the standpoint of the teaching staff and students. In connection with the strategic trends in the online education modernization in the study of mathematics and physics, psychological and pedagogical components are highlighted in the context of acmeology, synergetics, axiology
The Student-As-Consumer (SAC) Approach in Higher Education in Kazakhstan: Theory and Practice

After joining the Bologna process in 2010 the Kazakhstani system of education has transformed into 3 cycle training: Bachelor – Master – PhD. The process of integration of Kazakhstani universities into the international educational space has also begun. One of the main points of transformation was the attitude towards a student, which is declared by the student-based approach of the Bologna process, humanistic bases of modern theories of education and «student-as-consumer» approach (SAC). The article is aimed at detailing the features of a modern trend of promoting the concept of SAC (student-as-consumer) in the Kazakhstan, and the existing contradictions, or pressure, to grip the students' view in a time frame of 30 years of a new system of higher education, to propose recommendations for overcoming them in line with global trends and experience. Research methods: literature analysis and a survey of teachers and students. The article offers an overview of the «student-as-consumer» approach, which is associated with the emergence of market relations and the increase in personal financial contribution of students to their higher education. The focus of the study is to identify how students perceive current higher education and whether universities are in line with the «student-as-consumer» approach. The survey indicates that the attitude of a student as a consumer of educational services is still at the stage of formation. It also discusses the consequences of the «student-as-consumer» approach for both universities and students.
The Nutritional Classification of Pregnant Women Using Support Vector Machine (SVM)

Determining the nutritional status of pregnant women is one of the efforts to control the condition of pregnant women so that they can adjust their health conditions properly. The health condition of pregnant women can affect the condition of the baby who will be born. This study aims to apply the SVM method to a web-based application to classify the nutritional status of pregnant women based on data obtained from several health centers in the city of Lhokseumawe. SVM functions as the core of the application in charge of classifying the nutritional status of pregnant women based on several features including: age, weight, height, lila, hemoglobin and BMI. While the data class consists of 3 categories, namely: undernourished, normal nutrition and normal nutrition + overweight. Primary data obtained from the field amounted to 355 data which were then divided into two parts with a ratio of 70% training data and 30% testing data. Based on the research conducted, it was found that the application of different kernels in the Support Vector Machine (SVM) will have a different performance impact in classifying data. In this study, the linear kernel has the best performance with an accuracy value of 0.84, the RBF kernel has an accuracy value of 0.83, the polynomial kernel has an accuracy value of 0.72, and the sigmoid kernel has the worst performance with an accuracy value of 0.58
Performance Analysis of Naive Bayes Variation Method in Spice Image Classification Using Histogram of Gradient Oriented (HOG) Feature Extraction

Indonesia has a lot of natural wealth of spices. The diversity of spices is an inseparable aspect of Indonesian history. Spices and seasonings are biological resources that have long played an important role in human life. Indonesian spices have almost the same color and shape. The purpose of this study was to analyze the performance of the Naïve Bayes variation method in classifying spices using a Histogram Of Oriented Gradient (HOG) feature extraction. Based on 3 tests, the performance of the four Naïve Bayes variation methods carried out in this study, it can be seen that testing 5 types of spices using the Gaussian Naïve Bayes method obtained the best performance with an accuracy of 0.946, a precision of 0.95, a recall of 0.945, f1 score of 0.947, f beta score of 0.946, and Jaccard score of 0.90. Where as using the Complement Naïve Bayes method gets the lowest performance. From the results of this study it can be concluded that by utilizing HOG feature extraction and the Naïve Bayes variation method, maximum classification results are obtained in classifying spices. To obtain more accurate classification results, consider using other methods and other feature extraction
Implementation of the Naïve Bayes Method in Expert Systems for Diagnosing Achalasia

The purpose of this research is to solve the problem of diagnosing achalasia, which the general public does not yet know about the treatment procedures. Achalasia is a condition in which the muscles of the esophagus fail to push food or drink into the stomach. This condition can cause symptoms such as difficulty swallowing, regurgitation of food into the esophagus, and weight loss. The sample data used by the authors for the process of diagnosing achalasia in this study were obtained from experts (experts) through literature study data collection techniques and field studies (interviews and observations). The expert system method implemented by the author to solve the problem of diagnosing achalasia in this study is the Naïve Bayes method. Naive Bayes is a method that can provide a solution by determining the probability values of a phenomenon. The results showed that the Naïve Bayes method was able to diagnose achalasia quickly and easily with accurate and reliable solutions. So that it can be used as a relevant alternative to help ordinary people get solutions to cure Achalasia.
Optimizing Blockchain Network Creation: Automation with Ansible on Private Blockchain Hyperledger Fabric Using Simplified RAFT Consensus Method

In the rapidly evolving world of blockchain technology, efficient and reliable blockchain network creation poses a significant challenge. Manual processes in blockchain network setup often consume time, are prone to errors, and difficult to maintain. This research aims to optimize the creation of blockchain networks by leveraging Ansible automation tools on private blockchains using Hyperledger Fabric and implementing a simplified RAFT method. The approach involves configuring blockchain infrastructure with Ansible and integrating the simplified RAFT method into the private blockchain network. The test results demonstrate that the proposed approach significantly reduces the time required for blockchain network creation. In testing with a 92 Mbps internet connection, the time needed to create a blockchain network with 1 orderer and 1 peer with 44 connected channels, ready for transactions, was successfully reduced from 102.6 minutes to only 51.4 minutes. Moreover, the Ansible automation approach reduces the risk of errors and simplifies network maintenance. In conclusion, this research confirms the effectiveness of the proposed approach in optimizing the blockchain network creation process, reducing the required time, and enhancing efficiency and ease of maintenance. The proposed solution provides a valuable contribution to the development of efficient private blockchain infrastructure while minimizing errors and increasing flexibility.
Strategi Penanganan Speech Delay pada Anak: Literatur Review

Speech delay is a speech delay with development far below normal for children of their age. According to the Indonesian Pediatrician Association (IDAI), as many as 5 - 8% of preschoolers experience speech delays. Speech and language delays or disorders are becoming increasingly common today. In Jakarta, 21% of children experience speech delay. This literature review aims to find strategies for dealing with speech delay in children. The literature used is literature published within the last 5 years (2018 to 2022) and is primary research. The keywords used in this literature search were late speech, and children. Search using the Google Scholar search engine and Portal Garuda. The results of this literature review are strategies for handling speech delay in children that can be done are to involve the child to speak in every situation, invite the child to speak slowly, use correct and appropriate language and do it repeatedly, when the child is in the process of speaking, you should pay attention and observe every word or sentence that is spoken by children, justify sentences whose pronunciation is still wrong, and stimulate children's interpersonal communication so that children are more confident when carrying out the process of communication and interaction. Children can also be invited to do storytelling activities to improve vocabulary and speaking skills.