Reviewerly/exHarmony huggingface dataset:
Improved support vector machine classification for imbalanced medical datasets by novel hybrid sampling combining modified mega-trend-diffusion and bagging extreme learning machine model

&lt;abstract&gt; &lt;p&gt;To handle imbalanced datasets in machine learning or deep learning models, some studies suggest sampling techniques to generate virtual examples of minority classes to improve the models' prediction accuracy. However, for kernel-based support vector machines (SVM), some sampling methods suggest generating synthetic examples in an original data space rather than in a high-dimensional feature space. This may be ineffective in improving SVM classification for imbalanced datasets. To address this problem, we propose a novel hybrid sampling technique termed modified mega-trend-diffusion-extreme learning machine (MMTD-ELM) to effectively move the SVM decision boundary toward a region of the majority class. By this movement, the prediction of SVM for minority class examples can be improved. The proposed method combines α-cut fuzzy number method for screening representative examples of majority class and MMTD method for creating new examples of the minority class. Furthermore, we construct a bagging ELM model to monitor the similarity between new examples and original data. In this paper, four datasets are used to test the efficiency of the proposed MMTD-ELM method in imbalanced data prediction. Additionally, we deployed two SVM models to compare prediction performance of the proposed MMTD-ELM method with three state-of-the-art sampling techniques in terms of geometric mean (G-mean), F-measure (F1), index of balanced accuracy (IBA) and area under curve (AUC) metrics. Furthermore, paired t-test is used to elucidate whether the suggested method has statistically significant differences from the other sampling techniques in terms of the four evaluation metrics. The experimental results demonstrated that the proposed method achieves the best average values in terms of G-mean, F1, IBA and AUC. Overall, the suggested MMTD-ELM method outperforms these sampling methods for imbalanced datasets.&lt;/p&gt; &lt;/abstract&gt;
Granitoids Geological and Geochemical Features Study at Akchatau Massif

. The aim of the work is to identify the geological and geochemical features of the granitoids of the Akchatau massif. This article discusses the petrochemical data of the first and second phase introduction of the Akchatau massif, as well as the petrogenesis of rocks and the distribution conditions of tungsten and molybdenum. To diagnose the distribution of elements, the data were normalized by UCC. According to the results of the analysis, it was found that the chemical analysis of REE for granites is less than for leukogranites. It was also found that the REE distribution graph in the rocks of the Akchatau massif repeats the line of the upper crust.
Dual axis solar tracking system

&lt;span&gt;Now a days, many people use solar photovoltaic systems since they generate efficient and clean energy. Polycrystalline and monocrystalline silicon modules are currently the most widely used products in the solar industry. Aside from possessing a flawless lattice structure, a high level of material purity, a low grain boundary energy, a weak internal resistance, and a high level of efficiency, monocrystalline silicon cells also have a uniform colour and a lack of spots, which contribute to its good aesthetic appeal. By placing the solar panels at the precise angle and direction specified by the motion of the sun, the system's efficiency can be increased. The solar tracking system for this research project uses LDR sensors that are connected to a microcontroller to track the sun's horizontal and vertical axes, while DHT11 and rain sensors are used to track the weather. This study offers a method for repositioning a solar array so that it faces the sun at all times. Since solar modules effectively convert sunlight into electricity, they are helpful solutions to the problem of power generation in remote areas. A microprocessor is used in the construction of such a system to operate a motor and sensor.&lt;/span&gt;
Equivariant Representation Learning in the Presence of Stabilizers

We introduce Equivariant Isomorphic Networks (EquIN) – a method for learning representations that are equivariant with respect to general group actions over data. Differently from existing equivariant representation learners, EquIN is suitable for group actions that are not free, i.e., that stabilize data via nontrivial symmetries. EquIN is theoretically grounded in the orbit-stabilizer theorem from group theory. This guarantees that an ideal learner infers isomorphic representations while trained on equivariance alone and thus fully extracts the geometric structure of data. We provide an empirical investigation on image datasets with rotational symmetries and show that taking stabilizers into account improves the quality of the representations.
The principle of operation of a decentralized money transfer information system

The article is devoted to reviewing the concept of blockchain in the implementation of a decentralized money transfer system. The concept of fault tolerance of cryptocurrency technology is considered and some aspects of the fault tolerance solution are given. The analysis of information security of enterprises was carried out. The questions of anonymity of banking systems are touched upon. The dependence of the anonymity of the banking system on the quality of the software and on the security policy is shown. Recommendations are given for solving the problem of anonymity, in particular, generating a private key or embedding an algorithm for connecting transactions. Approaches to creating transactions are considered. Examples of algorithms that allow protecting user transactions are given. The description of the blockchain structure and the consensus algorithm has been carried out. The issues of generating a chain of blocks of an honest user and a chain of blocks of an attacker are considered. Formalization of the estimate of the probability of transaction break-even has been carried out. Binomial random walk was used for estimation. The end of the transaction process is just as justified as waiting for new blocks to be added to the chain of an honest user. A digital signature is used to identify whether a transaction belongs to a public key. The processes of signing a transaction and verifying a signature are considered. Separately, the approaches used in the design of cryptocurrencies are highlighted. The network stores the entire history of transactions, it is possible to calculate the balance of the total amount of incoming funds and spending money. These approaches are software solutions based on game theory and cryptography. The main attention is drawn to the business values of cryptocurrencies and ways to achieve them. As a result, the necessary functionality of the software client is formulated. The article provides a thorough analysis of a new financial and settlement instrument - electronic money. The task of developing a virtual payment system has recently received much attention.
PENERAPAN STRATEGI PEMETAAN PIKIRAN UNTUK MENINGKATKAN KEMAMPUAN MENULIS KARANGAN NARASI SISWA KELAS IV SDN PABEAN 2 SEDATI SIDOARJO

From the observation result and interview with Mr. Heru teacher of class IV-B at Pabean 2/65 Sidoarjo Elementary School, showing the ability of student in write narration essay at class IV-B of Pabean 2/65 Sidoarjo Elementary School still low. It can be seen from the score of student in make narration essay. Student feels difficult to find theme, express essay content, using spelling. It proved from the student score in making narration essay under SKBM, as big as 70. Only seven students (21%) from 32 students that reach above 70. Problems that become the background of those thing is the learning process that done by educator not successfully enough. The Indonesian learning limited only to product / result, concept and theory. In fact, many done traditionally. Model and method of learning that used still classical make Indonesian learning become boring to student. Teacher more played role on the learning process than student. It fell the relation between teacher and student and student each other not good connected yet. Related with those problems, researcher applying mind mapping strategy to increasing the write narration essay ability of fifth grade student of Pabean 2/65 Sidoarjo Elementary School. Hoped with mind mapping strategy, student can arising their idea to image, writing and color form. Next, student can make an essay frame in the interesting form then develop it on the narration essay. The kind of research is descriptive. The research design using class action research or PTK. This research done at two cycle and on each cycle consist of two meeting. Data collection technique using observation, test, and documentation. The execution of learning activity with aplication of mind mapping strategy done very good. The presentage of execution is increase each of that cycle. The average of student writing essay narration result score on the first cycle is 73.03 as big as 19 students (63.33%) from 30 student that present are fulfilling SKBM. On the second cycle the average score increase to 81.2 as big as 28 students (100%) from 28 students that present have fulfill SKBM. Problem that experienced is class conditioning that inappropriate, after finishing task student always runaway and disturbing each other, and student always must be reminded and guided about the spell writing. To overcome it with giving contract on the early of learning and student guiding patiently complete from start until the end.
Skeleton-Based Fall Detection Using Computer Vision

Falling is one of the biggest public health issues that can cause many serious long-term repercussions for patients and their families. In this paper, we propose an appropriate model for fall detection using graph convolutional networks. Recently, most problems related to human action recognition, including fall detection, can be handled by applying the spatio-temporal graph convolutional model using 2D or 3D skeletal data. We take advantage of the transfer learning technique from the NTU RGB + D consisting of 60 daily actions to extract features for fall detection tasks efficiently. Besides, to highlight critical frames in the original sequence, we suggest using a temporal attention module consisting of two parts: (1) an average global pooling, and (2) two fully connected layers. We conduct the test on two datasets, leading to a 3.12% increase in the TST dataset and a 2.67% improvement in the FallFree dataset. Notably, concerning the FallFree dataset, the model’s accuracy is up to 100%.
SmartOTP Solution for Small and Medium-Sized Businesses

With the rise of Web 2.0 and Web 3.0, personal information and identity have become a primary part of our lives, along with it, the security of said information. Most of the websites allow us to have our own user account to represent ourselves online. But with hacking and identity theft having no sign of decreasing, it is getting harder to trust those user accounts. As a result, identity authentication solutions have become a focus of attention for some time. One of the more focused solutions is One-Time Password (OTP). There are currently many commercial OTP solutions from many identity providers such as Amazon Web Service, Microsoft Azure, and Google Cloud that are complete and flexible to be applied to almost any use case. However, some businesses may require a simple and scalable solution that can quickly adapt to their needs. The paper presents such a solution for small and medium sized businesses. The paper describes the architecture and technologies applied to the solution to be able to achieve that goal. The approaches and results of the paper are completely applicable to businesses of any size.
Novice Behavior Analysis in Business Training by Using Tobii Eye Tracking Technology

Advanced technology exists in various business sectors. It is important to identify fundamental skills that are required in each business. This research aims to understand the situation or obstacles confronted by a novice staff individual while they perform required business tasks. This can provide an initial understanding of newly employed staff’s behavior and tailor meaningful training for them. Each employee performs required job tasks based on their previous background knowledge and experiences. To succeed in new job tasks, a proposed training guideline can assist novices get started and cooperate successfully with others throughout the new business environment. With the use of Tobii Eye Tracking Technology, the researcher can gather a novice’s behavior through eye tracking hardware—Tobii Glasses 3. This method discloses hidden thinking and level of task performance. Tobii Pro Lab is a software for doing analysis with 4 features: 1) gaze plots; 2) heat maps; 3) times of interest; 4) areas of interest to achieve the goal of providing information in training new employees.
Virtual Control System for Presentations by Real-Time Hand Gesture Recognition Based on Machine Learning

Presentations are a powerful tool for presenters who want to persuade their audiences in today's digital age. This paper exploits advances in hand gesture recognition, and proposes a virtual control system for presentations. The proposed system utilizes a webcam or built-in camera to capture hand gestures. Based on hand gestures, presentations can be controlled virtually and change presentation slides in both forward and backward directions. It is also possible by using the proposed system to get a pointer on the slide, write, or draw virtually on the screen through specific hand gestures. The obtained results show that the proposed system has a high accuracy of 96% in recognizing hand gestures and thus controlling presentations remotely without using any external device.
Waste Allocation Load Lifter Model for Trash Detection Based on Deep Learning and Wireless Localization Techniques

The progressive contamination of lands, oceans, and rivers by garbage is a growing concern. The pollution imposed by garbage accumulation can lead to serious health hazards as well as economic and environmental repercussions; this accumulation derives from irresponsible waste disposal. To counter the surplus of garbage pollution, this paper proposes an accurate, low-energy consuming system for a trash allocation robot, namely, Wall-E. Wall-E is a Waste Allocation Load Lifter Model for trash detection based on deep learning and wireless localization techniques. It is based on the integration of the deep learning models for trash detection and classification as well as a navigation system that is more accurate and energy-efficient than the commonly used global position system (GPS). The paper tests and analyses the neural network algorithms YOLOv7 and YOLOv8 conducted on a trash dataset and concludes that YOLOv7 is of higher performance in trash detection and classification. To implement this algorithm on a real-time robotic system, image frames will be sent from a Pi camera at a high frequency to a Raspberry Pi board that has YOLOv7 installed. In order to conceptually determine a suitable navigation system for our robot, the paper discusses the common challenges faced in traditional navigation systems, including proposed systems in related works and their limitations to our proposed system. In our conducted experiments, the proposed Wall-E achieved the highest accuracy in object detection using YOLOv7, which will be utilized in our robot to detect trash items sent through image frames from a Pi camera. The Dejavu navigation system was concluded as the most appropriate for our system’s requirements.
The Impact of Applying Deming's Model on Institutional Performance in Air Navigation: A Study on Quality and Customer Satisfaction

This study investigates the influence of applying Deming's model on the institutional performance of air navigation, with a specific focus on the concept of quality and its alignment with the present and future needs and expectations of customers. The research was conducted within the Department of Air Traffic in Kuwait, specifically within the Air Navigation Services Company, utilizing a sample of 206 employees. To achieve its objectives, the researcher developed a questionnaire as the primary research tool, designed to assess the relationship between the application of the Deming model and institutional performance. The study reveals several significant findings. Firstly, there exists a statistically significant relationship at a significance level of α≤0.05 between the practices of the Deming model and the promotion of administrative innovation within the Air Navigation Company in Kuwait. Additionally, the research demonstrates a statistically significant effect at the α≤0.05 level of Deming model practices on internal institutional innovation. Moreover, a similar statistically significant effect at α≤0.05 was observed for Deming Model Management practices on creating value within the Air Navigation Company in Kuwait. Among the key recommendations arising from this study is the importance of assessing the quality of air navigation services from the perspective of customers, particularly in light of the application of the Deming model. Understanding customer perceptions and expectations can play a pivotal role in further enhancing the quality and effectiveness of air navigation services. This research contributes valuable insights into the application of Deming's model in the context of air navigation, shedding light on its potential benefits in terms of innovation, quality, and value creation.
PREDICTING PARTICIPANT COMPETENCE TEST RESULT USING MACHINE LEARNING APPROACH

The 3in1 training for electronics operators aims to deliver competent participants. Therefore, it is necessary to improves the learning curriculum and materials. However, the organizer and teaching staff lack information about the factors that determined the success rate of participant's competence tests. Therefore, this paper aims to build a model for predicting participant competence test result. The data of participant's assessment scores were first collected and prepared as the dataset. The prediction model builds by applying a machine learning approach. These cover the use of ANOVA to ranked the course subjects towards the competency test results (C and NC) and build the prediction model using the Random Forest algorithm. From the results, we found that the competency test results are more affected by the practical subjects rather than theory subjects. From the ANOVA results, the most significant practical subject is the screwing lesson, while for theory subject is 5S-Kaizen. The prediction model obtains an accuracy of 94,6% for 5-subjects and 91,9% for 8-subjects from the original dataset. However, from the precision rate, it was found that the oversampling and hybrid sampling dataset shown better results. This confirms that the resampling technique is working to solve the imbalanced dataset problem.
The Applicability of Zipf's Law in Report Text

Zipf's Law, discovered by Harvard linguist Zipf in the 1940s, is an empirical Law reflecting the general rule of word frequency distribution in language. It describes the link between word frequency (F) and its frequency rank (R) as F×R=C (constant)[1]. A large number of studies have been conducted to test the applicability of Zipf's Law in various languages and diverse text types. However, as language keeps developing and changing, there is a continuous need for the verification of Zipf's Law in the most up-to-date materials and texts in different languages. In addition, different categories of texts should be taken into consideration. Based on this, this study selected the report of the 20th National Congress and its multilingual translations, which contain English, Spanish, and Russian, as our research objects. We used AntConc to detect the distribution of their word frequency and found that all of these report texts of four languages conform to Zipf's Law, and there is a slight difference in terms of fitness degree between the Russian text and the other three. We assumed that it is mainly caused by the limited text length and the translation process.
Preliminary Analysis of Double Limits

The limit of a univariate function is a critical concept in advanced mathematics, serving as the theoretical foundation for differentiation and integration. When it comes to the limit of bivariate functions, many students have limited exposure to bivariate functions during their high school years. This, coupled with the scarce explanations in textbooks, leads to some misconceptions in understanding the limit of bivariate functions. This paper delves into the concept of the limit of bivariate functions, introduces and solves the limit of bivariate functions by exemplifying the method of determining the limit of univariate functions; It also explains the double limit and iterated limit and distinguishes between them with practical examples, highlighting their connection through a theorem. By preliminarily analyzing the limit of bivariate functions, we hope to inspire math enthusiasts and deepen their understanding of double limits.
AN IMPLEMENTATION OF TWO-FACTOR AUTHENTICATION TECHNOLOGY USING TIME-BASED ONE TIME PASSWORD (TOTP) METHOD ON PRIVATE CLOUD STORAGE WEBSITE FOR GUIDANCE AND COUNSELING TEACHER

Guidance and counseling teacher, as an assistant profession, provides counseling services for all students. In providing counseling services, the first activity to be performed is the collection of students’ data. The data collected are confidential. Technological advances in private cloud storage media and two-factor authentication can help the guidance and counseling teacher in the fulfillment of students’ data storage and protection. Cloud storage is one of the solutions in dealing with data management so that it is centralized and facilitates access for system users. It is also balanced with the security technology on an application to guarantee the data security in the application. The security technology of two-factor authentication can be combined with a password and time-based one-time password. A time-based one-time password is often used on an application that uses two-factor authentication with the combination of a secret key and current timestamp. This study aims to create a private cloud storage website for guidance and counseling teacher with two-factor authentication using the time-based one-time password method. This website has been tested with black-box testing and user testing. From the results of the tests, a private cloud storage website with two-factor authentication using the time-based one-time password method was well accepted by the users because it helps the guidance and counseling teacher in sharing data safely, and the results of the average percentage in all questions were 85%.