Reviewerly/exHarmony huggingface dataset:
The Effect of Adaptive Synthetic and Information Gain on C4.5 and Naive Bayes in Imbalance Class Dataset

Class imbalance is a severe problem in classification due to the deep slope on the class axis. The dataset is dominated by the majority class, which has the potential for misclassification. Another problem in classification and clustering is that high-dimensional datasets are found that have the potential to affect the performance of classification algorithms in terms of computation and accuracy. In this study, the class imbalance was handled using the ADASYN k - NN resampling technique and the selection feature using Information Gain. Based on the evaluation results, the sampling contribution matrix can improve the classification model by improving the geometric mean value. The selection feature helps interpret data with more simple features but can reduce the accuracy of the results. The results showed that the implementation of ADASYN k-NN and Information Gain could increase the accuracy score and geometric mean score of Decision Tree C4.5 and Naive Bayes. For further work, this proposed method will be tested on multiclass imbalanced datasets.
Competence-Based Educational Paradigm in the Context of the Features of Its Practical Implementation in the Formation of Universal Competencies

The article discusses the main approaches to understanding the competence-based educational paradigm and some of the features and contradictions associated with the practical implementation of the competence-based approach. An example of evaluating residual knowledge and skills in the process of forming such a universal competence as the ability to carry out social interaction and realize their role in a team in three areas of bachelor&amp;apos;s training is given. The difficulties and contradictions associated with the management of professional and personal development of students at all levels of practical implementation of the competence approach are analyzed. The necessity of further research related to the development of valid and reliable methodological tools for assessing multi-level professional and personal substructures of orientation, social experience and cognitive abilities of students in the process of mastering various areas and profiles of training in higher school is substantiated.
FedCL: An Efficient Federated Unsupervised Learning for Model Sharing in IoT

Federated Learning (FL) continues to make significant advances, solving model sharing under privacy-preserving. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. As such, a natural problem is how to leverage unlabeled data among multiple clients to optimize sharing model. To address this shortcoming, we propose Federated Contrastive Learning (FedCL), an efficient federated learning method for unsupervised image classification. The proposed FedCL can be summarized in three steps: distributed federated pretraining of the local model using contrastive learning, supervised fine-tuning on a server with few labeled data, and distillation with unlabeled examples on each client for refining and transferring the personalized-specific knowledge. Extensive experiments show that our method outperforms all baseline methods by large margins, including 69.32% top-1 accuracy on CIFAR-10, 85.75% on SVHN, and 74.64% on Mini-ImageNet with the only use of 1% labels.
A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences

Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences
Better plain ViT baselines for ImageNet-1k

It is commonly accepted that the Vision Transformer model requires sophisticated regularization techniques to excel at ImageNet-1k scale data. Surprisingly, we find this is not the case and standard data augmentation is sufficient. This note presents a few minor modifications to the original Vision Transformer (ViT) vanilla training setting that dramatically improve the performance of plain ViT models. Notably, 90 epochs of training surpass 76% top-1 accuracy in under seven hours on a TPUv3-8, similar to the classic ResNet50 baseline, and 300 epochs of training reach 80% in less than one day.
Development of Android-Based Mobile Application Using Gyroscope Sensor

The idea of this project comes into the picture from various uses of sensors that are present in our surroundings. So, we can actually play with them by using two or more sensor mechanisms that end up as new inventions. Therefore, we are keen to look forward to one of the highly scoped and well-furnished sensors i.e., Gyroscope Sensor with an element of Light Sensor. This sensor is capable of measuring the orientation of the body and the angular velocity of the object. In our android project, we embedded the flashlight feature in the Gyro sensor’s angle alteration logic. This mechanism successfully leads to the flashlight of the camera getting ON &amp; OFF when the device is tilted or shifted along the reference angle. This collaboration of sensors plays an important role for a device that does not have that feature.
Denoising using Noise2Void for low-field magnetic resonance imaging: A phantom study

To reduce noise for low-field magnetic resonance imaging (MRI) using Noise2Void (N2V) and to demonstrate the N2V validity. N2V is one of the denoising convolutional neural network methods that allows the training of a model without a noiseless clean image. In this study, a kiwi fruit was scanned using a 0.35 Tesla MRI system, and the image qualities at pre- and postdenoising were evaluated. Structural similarity (SSIM), signal-to-noise ratio (SNR), and contrast ratio (CR) were measured, and visual assessment of noise and sharpness was observed. Both SSIM and SNR were significantly improved using N2V (P < 0.05). CR was unchanged between pre- and postdenoising images. The results of visual assessment for noise revealed higher scores in postdenoising images than that in predenoising images. The sharpness scores of postdenoising images were high when SNR was low. N2V provides effective noise reduction and is a useful denoising technique in low-field MRI.
ANALYSIS OF METHODS FOR OPTIMAL DESIGN OF MULTIFACTOR EXPERIMENT IN TERMS COST AND TIME CRITERIA

The object of research is the analysis of the state of development of methods of optimal planning of multifactorial experiment on cost and time indicators. The subject of the research is the methods of optimization of multifactor experiment plans in terms of cost and time indicators. The objective: the development of practical recommendations for the application of existing optimization methods of multifactorial experiment plans in terms of cost and time criteria based on their comparative analysis. The tasks are the comparing of optimization methods of multifactorial experiment plans by characteristics: the allowable number of factors for effective optimization, type of plan, accuracy of the method, the number of optimization criteria, speed; development of practical recommendations for the use of these methods; the determine of directions for further development of the research topic. Methods: method of comparative analysis, optimization methods based on the study of nature, combinatorial optimization methods, graph optimization methods, approximate optimization methods. The results of study. The 20 methods of multifactorial experiment plans in terms of cost and time criteria are analyzed. The 6 practical recommendations for their application in the range of factors number 2 &lt; k ≤ 16 are given. Conclusions. The scientific novelty of the obtained results is the improved comparative analysis of existing methods of multifactorial experiment plans based on 5 characteristics in terms of the experimenter's choice, namely: the allowable number of factors for effective optimization, type of plan, method accuracy, number of optimization criteria; speed-code. In the future, it is planned to study the classification of methods of multifactorial experiment plans, the development and improvement of two-criteria optimization methods for cost and time characteristics, the speed analysis of approximate optimization methods for k &gt; 7 and their improvement.
Hui2Vec: Learning Transaction Embedding Through High Utility Itemsets

Mining frequent itemsets (FIs) in transaction databases is a very popular task in data mining. It helps create meaningful and effective representations for customer transactions which is a key step in the process of transaction classification and clustering. To improve the quality of these representations, previous studies have adapted vector embedding methods to learn transaction embeddings from items and FIs. However, FIs are still a simple pattern type that ignores important information about transactions such as the purchase quantities of items and their unit profits. To address this issue, we propose to learn transaction embeddings from items and high-utility itemsets (HUIs), a more general pattern type. Since HUIs were shown to be more appropriate than FIs for a wide range of applications, we take for hypothesis that transaction embeddings learned from HUIs will be more representative and meaningful. We introduce an unsupervised method, named Hui2Vec, to learn transaction embeddings by combining both singleton items and HUIs. We demonstrate the superior quality of the embedding achieved with the proposed method compared to the embeddings learned from items and FIs on four datasets.
Psychological Features of the Relationship Between Hardiness and Anxiety

The article describes the hardiness of the individual. It is represented by a set of attitudes, skills that provide courage, courage, bravery, and strategies that should turn stressors into opportunities for growth. Three main components are revealed: risk involvement, control and acceptance and their functions. It is determined that a person who has a high level of hardiness has a stable psychological and physical health, well adapted to the social space. It is noted that hardiness exists on three levels: psychophysiological (optimal responses to stress), socio-psychological (effective self-regulation) and personal-semantic (meaningfulness and positive worldview). Also, the essence of personality anxiety is characterized, it is presented as a certain emotional state of a person that arises under conditions of probable surprises in the case of postponement, delay of pleasant situations, and while waiting for trouble. It was found that anxiety can be both situational and personal. It is emphasized that situational anxiety arises from a stressful state or stimulus that affects a person at a particular time, and personal anxiety, in turn, is a personality trait that characterizes a person as prone to constant anxiety. Also identified and characterized are anxiety, neurotic, and moral anxiety. It is emphasized that anxiety can be a factor in mental problems, neuroses and fears. The article analyzes the results of an empirical study on the peculiarities of the manifestation of hardiness and anxiety of students. In particular, it was found that the average level of hardiness prevails among young people (45%) and the average level of anxiety, which tends to be high (33%). There is also a direct correlation between students' resilience and anxiety. It was proved that the subjects who had a low level of hardiness had a high level of anxiety, and the subjects who, in turn, had a high and medium level of hardiness, had a low or medium level of anxiety, which tended to low.
THE ESSENCE AND STRUCTURE OF THE FUTURE TEACHER’S READINESS FOR PARTNERSHIP IN PRIMARY SCHOOL

The relevance of the article is due to the problem of the quality of training of future primary school teachers in the conditions of modern education reform. A modern primary school teacher is an agent of changes in the modern educational space in the conditions of the formation of the “New Ukrainian School” Concept, whois able to build special relationships with the participants of the educational process on the basis of partnership. The purpose of the article is to determine the essence and structure of the future teacher’s readiness for partnership interaction in primary school. The purpose of the article is to justify the need to form a future teacher’s readiness for partnership interaction in primary school, as well as to determine the essential content of the concept “future teacher’s readiness for partnership interaction in primary school” and the structure of this phenomenon. In the course of the study, various approaches to defining the concept of “readiness of the future teacher for partnership interaction in primary school” were analyzed, the author’s definition of the mentioned phenomenon was presented. It was established that the readiness of the future teacher for partnership interaction is a set of personal and professional qualities that contribute to effective interaction between the subjects of the educational process on the basis of partnership. The future teacher’s readiness for partnership interaction in primary school includes the following components: motivational and value (includes motives, needs for partnership interaction, universal and educational values); cognitive (reflects knowledge about partnership interaction with participants in the educational process, its essence, structure, criteria and levels of formation, interaction technologies); praxeological (mastery of communicative and organizational skills in the process of partner interaction; ability to apply effective strategies, technologies, forms and means of partner interaction; ability to perform reflective analysis of one’s activities). We see prospects for further research in determining the criteria, indicators and levels of readiness of future teachers for partnership interaction in primary school
USE OF ICT AND MEDIA RESOURCES BY STUDENTS OF PHILOLOGY IN THE EDUCATIONAL PROCESS OF HIGH SCHOOL: MODELS AND STRATEGIES.

Thearticledealswiththeeffectivenessoftheuseof ICTandmediaresourcesbyphilologystudentsintheeducational process of higher education. The authors set the goal of investigating the features of the effective use of ICT and, in particular, media resources by philology students in the educational process of higher education. The tasks consist in the analysis of modern studies of the outlined problems, in the search for effective media resources and models of the implementation of information technologies in order to improve the quality of the educational process. It is assumed that thanks to the use of computer networks, online learning tools, personal offices, Telegram-channels and chat-bots, teachers have been given the opportunity to provide new information in such a way as to satisfy the individual requests of each student, because today in a short period of time it is necessary to teach each child to assimilate, understand, transform, analyze and use large volumes of information in practical activities. The main directions in which the active search and selection of effective models of information technology implementation are carried out in order to improve the educational process are characterized: development of the educational environment; use of innovative teaching aids; introduction of the latest learning technologies in the educational process; modernization of organizational forms of educational activity. Attention is focused on the fact that currently it is necessary to focus on one’s own professional development, the peculiarities of the educational e-environment and improving the quality of providing educational services with the help of ICT and media resources, increasing the interest and activity of education seekers, etc. It is noted that the survey of the participants of the educational process remains one of the most successful and widespread methods of evaluating the optimization of language and literature learning with the help of media resources
Pengenalan Teknologi Android Game Edukasi Belajar Aksara Sunda untuk Meningkatkan Pengetahuan

The rapid development of technology covers many fields that are interconnected with each other, especially in the field of education where when a technology is applied, the learning process is not only limited to between teachers and students but students can access learning through various media. The lack of learning systems still use traditional methods, giving the impression of being bored. From the problems that occur causing a lack of knowledge of children about Sundanese script, due to lack of knowledge in recognizing Sundanese script can result in the loss of regional languages in Indonesia because there are too many elements of language and culture from outside. By making Sundanese script educational games using the ADDIE method, in order to introduce new technology to recognize Sundanese characters. An Android-based educational game that can be used anywhere and anytime and eliminates boredom during the learning process. The results of the research evaluation with the whitebox and blackbox testing process. Whitebox testing is carried out for game use paths, in the blackbox testing process it meets the requirements where tools, buttons can work well. The results of processing the questionnaire data obtained the percentage of attitude toward behavior (X1) of 87%, subjective norms (X2) of 89%, behavioral intention (X3) of 94%, and behavior (Y1) of 91% of the 30 respondents. From this percentage, it can be concluded that this application is able to increase student interest in learning so that it can increase the knowledge of students at SMPN 7 Cirebon City.
Sistem Informasi Penjualan Motor Bekas Berbasis Android Untuk Meningkatkan Penjualan di Mokascirebon.Com

Mokascirebon.com is a trusted showroom in Cirebon for buying and selling second hand motorcycle, where in the business of buying and selling second hand motorcycle, it requires a large amount of capital and must be able to turn in and out of the motorcycle quickly considering that the longer the motorcycle is sold, the price will be decreased, and also if the tax of motorcycle stocks are in due date, it will increase the capital costs which is needed. The length of time a motorcycle has been sold has many factors, including tight business competition, where all of them are competing to increase sales of their second hand motorcycle and also have a system that makes working faster and more precise. From those problems, an idea is emerged to increase the motorcycle sales by increasing the reach of promotional media on the Android platform and creating an Android based computerized system that is able to accelerate the internal work of the company. In designing this application using the waterfall method, where the stages are gathering needs, building and evaluating waterfall, coding, testing, evaluating the system until finally the system can be used. The programming language used is Java and also uses the Android Studio application to create the Android application. The database used is MySQL. The supporting tools used are Photoshop, Corel Draw and the Android Emulator. With this android based second hand motorcycle sales information system to Increase Sales at Mokascirebon.com, it is hoped that it will be able to increase sales of second hand motorcycle and to simplify and accelerate the company's internal work
Prediksi Tingkat Risiko Kredit dengan Data Mining Menggunakan Algoritma Decision Tree C.45

Finance companies in providing credit conduct data analysis first to reduce credit risk. When customers do not pay credit smoothly, it will harm the company. For this reason, credit analysis is an important factor to minimize financial risk. So, it takes a predictive analysis of the level of credit risk based on data or files from customers. This study aims to predict the level of credit risk with data mining using the C.45 decision tree algorithm. There are two classes of risk level predictions, namely current and non-current. The C.45 decision tree algorithm has a function to find knowledge or patterns of characteristic similarity in a particular group or class. In this study, the C.45 algorithm was implemented and analyzed using the WEKA application. From the results of the evaluation using the confusion matrix, the accuracy generated for 1,153 training data with 91 testing data and the six attributes used produces an accuracy of 79%
ADVANCED ALGORITHM OF EVOLUTION STRATEGIES OF COVARIATION MATRIX ADAPTATION

The paper considers the extension of the CMA-ES algorithm using mixtures of distributions for finding optimal hyperparameters of neural networks. Hyperparameter optimization, formulated as the optimization of the black box objective function, which is a necessary condition for automation and high performance of machine learning approaches. CMA-ES is an efficient optimization algorithm without derivatives, one of the alternatives in the combination of hyperparameter optimization methods. The developed algorithm is based on the assumption of a multi-peak density distribution of the parameters of complex systems. Compared to other optimization methods, CMA-ES is computationally inexpensive and supports parallel computations. Research results show that CMA-ES can be competitive, especially in the concurrent assessment mode. However, a much broader and more detailed comparison is still needed, which will include more test tasks and various modifications, such as adding constraints. Based on the Monte Carlo method, it was shown that the new algorithm will improve the search for optimal hyperparameters by an average of 12%.
Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions

Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects. In particular, modelling cause-and-effect relations between the latter can help to predict unobserved human behaviours and anticipate the outcome of specific robot interventions. In this paper, we propose an application of causal discovery methods to model human-robot spatial interactions, trying to understand human behaviours from real-world sensor data in two possible scenarios: humans interacting with the environment, and humans interacting with obstacles. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm in some challenging human environments, with potential application in many service robotics scenarios. To demonstrate the utility of the causal models obtained from real-world datasets, we present a comparison between causal and non-causal prediction approaches. Our results show that the causal model correctly captures the underlying interactions of the considered scenarios and improves its prediction accuracy.