Reviewerly/exHarmony huggingface dataset:
Analysis of factors influencing the academic education of future bachelors in Electronics in Ukraine

The relevance of the chosen topic is determined by its importance in the context of the modern professional education system in Ukraine. This is influenced by contemporary trends: the need for highly qualified specialists, the improvement of the quality of educational services, and the implementation of innovations. Accordingly, the growing role of electronics in the modern world creates the need for highly qualified bachelors in Electronics. Analyzing the factors that affect the quality of academic education in this field will help identify key aspects that will contribute to the training of competent specialists. The purpose of the research is to investigate various factors that influence the quality and effectiveness of academic education for students pursuing a degree in "Electronics" in Ukraine. Considering the multifaceted manifestations, functional ambiguity of factors in the academic education of future bachelors in Electronics, their analysis required the use of a range of methods: search-oriented, analytical, comparative, systematization, documentation analysis, systemic, and synergetic. Within the framework of the research, three reports on the results of accreditation expertise of the bachelor's degree educational program in specialty 171 "Electronics" were selected from the State University of Intellectual Technologies and Communications, the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", and the National Aviation University. Based on the obtained results, it is worth noting that one of the key factors that can influence the quality of the academic training aimed at students is the qualification of teachers. The factor of the availability of the possibility of forming an individual educational trajectory for students is a relatively new but quite a relevant one. Another important factor is the quality of the educational program, which is the foundation for the activities at higher education institutions. The state of educational infrastructure, including the availability of modern equipment and laboratories that can help students acquire practical skills in Electronics, is no less important. It is appropriate to note that the listed factors can interact and influence the academic success of future bachelors in Electronics in Ukraine. To ensure students’ success, it is essential that universities properly take these factors into account and create favorable conditions for learning and development.
A Study on Swarm Intelligence and Its Various Clustering Algorithms in Medical Diagnosis

Swarm intelligence is a manner the usage of the knowledge of collective objects (human beings, bugs, etc.) collectively to attain the optimized solution for a given hassle. “Swarm” is a set of objects (people, bugs, and so on.). In different phrases, permits say we supply a troubling statement to a single person and tell her or him to undergo this hassle after which provide the solution, then because of this we will keep in mind the solution of that specific character only; however, the problem is that the answer given with the aid of that man or woman might not be the best solution or maybe that solution is not proper for others. So, to keep away from that, what we do is we give that hassle to a certain quantity of humans together (swarm) and ask them to attain the first-class answer feasible for that problem and then compute all the responses together to reach the high-quality solution possible.
Who’s Who in Mineral Names: Robert M. Hazen (b. 1948)

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSI thank Malcolm Southwood for his constructive comments on an earlier version of the manuscript.Additional informationNotes on contributorsEdward S. GrewDr. Edward S. Grew is a research professor in the School of Earth and Climate Sciences at the University of Maine, and since 2008 he has coauthored papers with Robert M. Hazen on mineral evolution and mineral ecology.
Aggressive Social Media Use Detection Based on Deep Learning

The prevalence of cyber aggressive comments on social networks, particularly among adolescents, has been increasing steadily as users spend more time connecting with others, sharing information, and pursuing common interests. Recent research has explored various deep learning models for detecting cyber aggressive comments, leading to efficient identification mechanisms compared to standard methods. This chapter proposes a deep belief network model specifically designed for cyber aggressive detection in social media comments. The data pre-processing stage involves text cleaning, tokenization, stemming, lemmatization, and the removal of stop words. The cleaned textual data is then fed into a deep belief network model for prediction. The experimental results demonstrate the high accuracy achieved by the deep belief network model.
Harnessing the Potential of Central Bank Digital Currency: A Case Study on Current Realities in Nigeria

About 33% of the over 200 million Nigerian population reportedly dealt in cryptocurrencies between November 2020 and April 2021. Yet only 0.5% of the same population have accessed the country’s Central Bank Digital Currency (“eNaira”) platform launched over a year ago. The eNaira is a digital currency backed by the government of Nigeria. Its primary aim is to improve the country’s payment system by creating a digital complement to the physical fiat currency (the “Naira”). It also seeks to advance the boundaries of payment systems in Nigeria, enabling seamless and easier financial transactions across borders.While the eNaira uses the same blockchain technology as cryptocurrencies such as Bitcoin and Ethereum and can be stored in a digital wallet, unlike the latter two, it is not decentralised nor a financial asset in itself, but merely a digital form of the Naira. Despite its limitations, the eNaira project is a laudable one whose potential can be fully harnessed.
Enhancing Sentiment Analysis Using Syntactic Patterns

Using specialized analysis tools, combining natural language processing techniques with machine-learning-based sentiment analysis, it is possible to establish positive and negative sentiments expressed in opinion texts. Thus, organizations have the possibility to act in an adequate way, having the opportunity to improve their relationship with their customers and improve their loyalty, according to the type of sentiment identified. In this paper we present and describe a sentiment analysis system especially developed to identify sentiments, of different polarities, expressed in opinion texts of students of an eLearning application. We have slightly rewritten the usual way of approaching sentiment analysis problems by using Hearst patterns, for improving classification models efficiency, valuing the sentiments expressed in a wider scale of classification values.
Computer Image Intelligent Recognition System Based on Visual Communication Technology

Computer technology is developing rapidly, and image recognition systems are becoming more and more important in human life. People obtain data through visual sensors and transmit it to the brain. The key to image recognition is to establish an efficient, real-time, and accurate three-dimensional digital model. However, due to different environments and backgrounds and noise interference factors, there is inevitably a certain degree of difference in the processing process, so the efficiency is low and unstable, which causes the recognized image to be too blurred, which affects the actual work’s efficiency. The response time of the system (TS) is 36, 39, 26, and 51 ms, and the recognition accuracy rates are 96, 93, 89, and 91%, respectively. It can see that the response time of this experimental test is relatively short and the image recognition accuracy is high, which can meet the basic requirements of intelligent image recognition.
Feature Selection for Performance Estimation of Machine Learning Workflows

Performance prediction of machine learning models can speed up automated machine learning procedures and it can be also incorporated into model recommendation algorithms. We propose a meta-learning framework that utilizes information about previous runs of machine learning workflows on benchmark tasks. We extract features describing the workflows and meta-data about tasks, and combine them to train a regressor for performance prediction. This way, we obtain the model performance prediction without any training, just by means of feature extraction and inference via the regressor. The approach is tested on OpenML-CC18 Curated Classification benchmark estimating the 75th percentile value of area under the ROC curve (AUC) of the classifiers. We were able to obtain consistent predictions with $$R^2$$ score of 0.8 for previously unseen data.
AI Pathfinding Algorithm in 3D Game Development Strategy Optimization System

Nowadays, the development speed of computer technology is accelerating, and in the process, the game industry has developed into an emerging entertainment industry with a large market in my country. With the rapid development of the game industry, some related technologies also show a trend of rapid development. The main purpose of this paper is to study the application of artificial intelligence pathfinding algorithm in 3D game development strategy optimization system. This paper firstly studies pathfinding and analyzes basic mobile pathfinding, simple obstacle-avoiding pathfinding, and waypoint pathfinding; then introduces the A* algorithm, including three types of heuristic functions: Manhattan distance, Chebyshev distance, and Euclidean distance. After testing various search algorithms without obstacles and with obstacles, the experiments show that the A* algorithm is the most suitable for game pathfinding operations. The game path, it searches, is very smooth and intelligent, and the consumption is very high less CPU time.
Peran Mendeley dalam Meningkatkan Kemampuan Menulis Karya Ilmiah Guru-guru di Kabupaten Lombok Barat

Publication of scientific work is mandatory for teachers seeking promotion as a requirement to enhance their professionalism. Citing scientific works is crucial in every publication. However, many teachers often encounter difficulties in finding and managing references to be used in research publications. The objective of the Community Engagement Activity is to assist teachers in effectively and efficiently searching for references and organizing the sources to be used as references in their publications. Mendeley, as a reference management application, plays a significant role in improving the ability to write academic papers by providing features that facilitate reference searching and publication writing. The training is conducted through lectures, discussions, and practical exercises. The initial stage involves introducing the Mendeley application and installing it on participants' laptops. Participants are also taught how to collect references in Mendeley for later use in academic writing and how to apply Mendeley in the writing process. The outcome of this engagement is that teachers in West Lombok Regency can effectively master the use of the Mendeley application, thereby supporting their academic writing activities. With these skills, teachers will find it easier to search for references and streamline the process of creating automatic bibliographies
Simulation of sports training recognition system based on internet of things video behavior analysis

The video behavior analysis of the Internet of Things is the apparent characteristics and continuous state of the two-dimensional human body in the time dimension. The patterns representing human behavior can be divided into states based on human body structure, forms based on common features and forms based on temporal features, local spaces, and behaviors based on deep learning. Applying IoT video behavior analysis to sports training will have a huge impact. Physical training is the process of training people to improve their physical fitness, increase courage and endurance, and acquire practical skills. It is the basis of various training courses and an important way to improve human physical fitness. The motion recognition system is a DTW pattern matching method, which uses a finite state to describe the spatial characteristics of the angle between the joint points. This method can accurately meet the low-delay requirements of the motion recognition of each frame and the standard motion. It can match and eliminate the influence of individual differences on motion recognition. At the same time, it can also effectively expand the pattern matching method to recognize newly filled motions, and it is a powerful composite general motion recognition system.
SCFANet: Semantics and Context Feature Aggregation Network for 360° Salient Object Detection

How to solve the problem of geometric distortion is the key for salient object detection (SOD) in 360° omnidirectional images. Most of the current methods integrate global and local visual cues through the fusion of the 360° equirectangular images and corresponding 360° cube-map images. The fusion in a single level cannot effectively utilize the information between the 360° equirectangular images and corresponding 360° cube-map images. In this work, we innovatively propose a semantics and context feature aggregation network (SCFANet) by fully exploring the interactivity between the two projection data. Specifically, we use Vision Transformer (ViT) to capture global visual cues for 360° equirectangular images and Convolutional Neural Network (CNN) to capture local visual cues for 360° cube-map images. To achieve effective fusion of the two projection data, we design a semantic guidance module (SGM), in which semantic features are used to guide the information fusion of the 360° equirectangular images and corresponding 360° cube-map images at each level. Then, a context fusion module (CFM) containing one local input and two context inputs is designed to integrate multi-scale features, where the local input extracts its own multi-scale information, and the context inputs complements their fine details and location information. Finally, we use feature aggregation and refinement module (FARM) to aggregate semantics and context feature and adopt a deep supervision strategy for training. Extensive experiments on two public 360° datasets show that our SCFANet exhibits competitive performance compared to other state-of-the-art (SOTA) 360° salient object detection models.
Governing Online Forum Interactions with Multi-agent System: A Reddit Use Case with the JaCaMo Platform

Autonomous agents are intelligent software systems that can carry out tasks on behalf of users without their intervention. On online forums, autonomous agents can operate to read and post messages on behalf of users. The proper regulation of agents’ interactions are required for achieving the expected results on online forums. One way to govern these interactions is using multi-agent oriented programming platforms. Here we introduce the use of JaCaMo, a multi-agent oriented programming platform, for governing agents’ interactions within online forums. We show its feasibility for regulating agents’ interactions within a popular online forum, Reddit.
PENGEMBANGAN MEDIA BERBASIS ANDROID PADA MATA PELAJARAN DESAIN MULTIMEDIA INTERAKTIF (DMI) DI SMK NEGERI 1 SIJUNJUNG

The goal to be achieved in this study is to find out the valid and practical characteristics of the Interactive Multimedia Design subject at SMK Negeri 1 Sijunjung, the method used is Research and Development (R&amp;D), while the development model used is the ADDIE development model, namely the development which consists of five stages, namely, Analysis (analysis), Design (design), Development (development), Implementation (implementation), and Evaluation (evaluation). Research on the development of the ADDIE model was carried out only up to the Development stage, because the purpose of this research was only limited to developing and producing a valid and practical learning media, to be implemented. Making learning media in this Android-based interactive multimedia design subject uses the MIT App Inventor. The validity test of this Android-based learning media was carried out by 3 validators, namely the media validator and the material validator, for the media validator it was carried out by 3 lecturers, while the material validation was carried out by 1 lecturer and 2 teachers who teach interactive multimedia design subjects at SMKN 1 Sijunjung. the validation results of media experts were 0.903% in the valid category and material experts were 0.859% in the valid category. Android-based learning media is declared valid. The practicality Test of Android-based learning media from teacher practicality data is 91.66% in the very practical category.
Adaptive password guessing: learning language, nationality and dataset source

Abstract Human chosen passwords are often predictable. Research has shown that users of similar demographics or choosing passwords for the same website will often choose similar passwords. This knowledge is leveraged by human password guessers who use it to tailor their attacks. In this paper, we demonstrate that a learning algorithm can actively learn these same characteristics of the passwords as it is guessing and that it can leverage this information to adaptively improve its guessing. Furthermore, we show that if we split our candidate wordlists based on these characteristics, then a multi-armed bandit style guessing algorithm can adaptively choose to guess from the wordlist which will maximise successes.
Privacy-Preserving Genomic Statistical Analysis Under Local Differential Privacy

As the amount of personal genomic information and privacy concerns in data publication have been growing, several studies have pointed out that the presence information of a particular individual could be revealed from the statistics obtained in large-scale genomic analyses. Existing methods for releasing genome statistics under differential privacy do not prevent the leakage of personal information by untrusted data collectors. In addition, the existing studies for statistical tests using a contingency table had restrictions on the number of cases and controls. Moreover, the methods for correcting for population stratification cannot protect genotype information. Thus, developing a more general and stronger method is desired. In this study, we present privacy-preserving methods for releasing key genome statistics. Our methods enhance the randomized response technique and guarantee individuals’ privacy, even when untrusted data collectors exist. Moreover, our methods do not require any restrictions on the contingency tables, and they also guarantee the privacy of targeted genotype information for the analyses to correct for population stratification. The experimental results indicate that our methods can achieve comparable high accuracy to existing methods while preserving privacy more strictly from any data collectors. Furthermore, for statistical analysis using a contingency table, we consider the case where different privacy budgets are assigned to each of the row and column information, and present optimal methods in terms of privacy assurance for the entire table that outperform the existing method. Overall, this study is the first step toward genomic statistical analysis under local differential privacy. The Python implementation of our experiments and Supplementary Material are available at https://github.com/ay0408/LDP-genome-statistics .
Stable skyrmion lattices found in composite multiferroics with neural networks

Magnetoelectric nanofilms are of great interest as functional elements of ultra-dense memory cells. In the ground state they may contain various topological magnetic vortex structures of several nanometers in size. The qualitative and quantitative properties of such structures strongly depend on a set of physical parameters. To calculate the ground state configuration with given parameters, we use the steepest descent method; to study a large parametric space, however, significant computational resources are required. To solve this problem, we propose the use of artificial neural networks (ANN), which can help us uncover the relationship between combinations of parameters and the corresponding ground state configurations, using a relatively small number of pre-computed configurations as training data. The application of the ANN allows one to avoid excessive computational costs in the study of the parametric space and narrow down the parametric area in which the existence of stable non-trivial ground state configurations in the form of a stable skyrmion crystal is possible.
Leveling Up the Exergame – An Attractive and Effective Training Approach for Body and Brain

Today’s widespread social tendency towards physical inactivity is being accompanied by a call for alternative, innovative methods that provide easy access and adherence to a physically active lifestyle. Over the years, exergames have established themselves as promising training tools. Numerous studies have verified the potential of exergames to improve cognitive, physical, and mental health while providing a motivating and appealing experience. At the same time, researchers and designers are still working on exploiting the full potential of the innovative training tools since evidence on long-term motivation and training effects is still limited. To contribute to this topic, insights from two exergame projects are presented.
International Development of the GeoCapabilities Project as a Geography Teacher Training Program and Acceptance Process in Japan

2012년부터 미국, 영국, 핀란드 등, 유럽 국가를 중심으로 진행되어 온 지오캐퍼빌리티즈 프로젝트는 다양한 교과 안에서 이루어지고 있는 각국의 지리교육에 공통으로 적용할 수 있는 국제적 지리교육체계의 개발을 위한 이론을 구축하고, 이를 바탕으로 전개된 지리교원양성 프로그램을 제공하고 있다. 이와 관련하여 본 연구는 일본프로젝트에 직접 참여한 본 연구자의 실행연구로 수행되었다. 국제프로젝트와 비교하면 일본프로젝트에서는 기존의 수업연구에 익숙한 교사들이 주도적 역할을 가지고 참여하고 있으며, 교사들은 커리큘럼 메이킹과 강력한 학문적 지식의 이론을 통해 평소 자신의 수업을 되돌아보는 계기가 되었다. 더욱이 2018년 개정 고등학교 학습지도요령부터 새로운 필수과목으로 지정된 지리총합을 준비하는 과정에서, 지리를 전공으로 하지 않는 교사가 지리총합을 담당하는 경우를 대비하여, 역사나 공민(일반사회)을 전공한 교사를 대상으로도 교원연수가 진행되었다. 하지만 앞으로 사회적 정의를 바탕으로 일본의 사회적 이슈를 주제로 한 수업실천이 기대되는 가운데, 연수에 참여한 교사들이나 이론연구를 수행하고 있는 지리교육연구자 사이에서 캐퍼빌리티적 접근에 대한 충분한 공통적 이해가 이루어지지 못하고 있다. 이는 앞으로의 프로젝트 진행에 있어서 무엇보다 중요한 과제라 할 수 있다.Since 2012, GeoCapabilities project has been carried out mainly in Western countries. The project is a geography teacher training program based on some theories for developing an international framework that can be commonly applied to geography education in various countries. This paper was conducted as an action research by a researcher directly participating in the Japanese project introduced from 2016. Compared to the international project Japanese project has the following characteristics teachers who have been familiar with lesson studies participated in the project as a leading role. Through the theories of Curriculum Making and Powerful Disciplinary Knowledge teachers were inspired to reflect on their usual lessons. Teacher training was conducted for practices of Chiri Sougo, which was designated as a new compulsory subject from the 2018 revision of the high school curriculum guidelines. It also included history and civics teachers to prepare for the case where a teacher who does not specialize in geography. In addition, considering social justice in the future, it is hoped that classes on social issues will be practiced in Japan. However common understanding of Capabilities Approach has not yet been obtained among teachers who have participated in teacher training and geography education researchers who are conducting theoretical research. This will be the most important issue in the future progress of the project.