Reviewerly/exHarmony huggingface dataset:
COMPARISON OF KNN, NAIVE BAYES, DECISION TREE, ENSEMBLE, REGRESSION METHODS FOR INCOME PREDICTION

Using the income classification dataset, we performed data analysis with the help of data mining to gather interesting information from the available data. Currently, data processing can be done using many tools. One of the tools that we use for data processing is the orange application. By using the dataset we looked at the welfare level ranging from marital status, school, gender, and from all fields related to income ranging from sales, to daily life to find out the income earned by employees or workers from several countries such as the United States, Cambodia, United Kingdom, Puerto-Rico, Canada, Germany, Outer US (Guam-USVI-etc). The purpose of this analysis is to determine the hourly income in one week that can affect the income classification. The classification technique uses various classification models, namely the K-Nearest Neighbor (KNN) algorithm model, Naïve Bayes, Decision Tree, Esemble Method and Linear Regression algorithm. The results of the analysis based on the test results of various algorithm models can be concluded that the best algorithm model for measuring workers' income is to use the Naive Bayes Decision. Analysis of variables based on Hours-per-Week and Capital-Gain affects Income Classification which determines whether the income earned is more than 50 thousand/50 K and the analysis results in a prediction of a person's income level.
Organization of distance learning using the Moodle platform, the Zoom service, and Google digital tools for higher education students of the Aquatic Bioresources and Aquaculture educational programs at Bila Tserkva National Agrarian University

Today, students of higher education study in Ukraine; it is not easy to imagine without information technologies, which are necessary for modern education. Creating a global information space, such technologies penetrate all spheres of human activity, thus becoming an integral part of the educational process. In today's challenges, pandemics, and wars, modern education relies on the informatization of the educational space and distance learning, drawing society's attention to distance learning technologies. During martial law, remote course training in schools and universities makes it possible to receive and continue learning, gain knowledge, and increase training. Using remote platforms to learn educational components is an integral part of the educational process. Training specialists, training, and counseling of higher education seekers in the existing conditions is only possible with modern educational online platforms. The main task is the correct organization of the educational process and for those seeking higher education to successfully acquire the necessary knowledge in specialized disciplines to prepare competent, competitive specialists. Bila Tserkva National Agrarian University applies a distance form of education, which is regulated by regulations (“Regulations on the system of internal quality assurance of education and educational activities at Bila Tserkva National Agrarian University”, “Regulations on the organization of current, semester control and attestation of applicants of professional pre-higher and higher education with the use of distance learning technologies in the regional university center of Bila Tserkva National Agrarian University”, “Regulations on the Moodle learning management system at Bila Tserkva National Agrarian University”). Distance learning in recent years has shown a sufficiently high-quality success rate of higher education applicants and the training of scientific and pedagogical personnel to ensure the educational program Water bioresources and aquaculture at the Bila Tserkva National Agrarian University. Students are provided with electronic versions of manuals, textbooks, lecture courses, and presentations on the Moodle platform and other services. The use of Google digital tools and the Moodle platform, the Zoom service help students of higher education acquire knowledge in their field in an accessible form and comfortable conditions.
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not available. A straightforward direction is to employ the widely used Infomax technique from typical Unsupervised Graph Representation Learning (UGRL) to learn robust unsupervised representations. Nonetheless, directly transplanting the Infomax technique from typical UGRL to robust UGRL may involve a biased assumption. In light of the limitation of Infomax, we propose a novel unbiased robust UGRL method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Robust Graph Information Bottleneck</i> (RGIB), which is grounded in the Information Bottleneck (IB) principle. Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph. There are mainly two challenges to optimizing RGIB: 1) high complexity of adversarial attack to perturb node features and graph structure jointly in the training procedure; 2) mutual information estimation upon adversarially attacked graphs. To tackle these problems, we further propose an efficient adversarial training strategy with only feature perturbations and an effective mutual information estimator with the subgraph-level summary. Moreover, we theoretically establish a connection between our proposed RGIB and the robustness of downstream classifiers, revealing that RGIB can provide a lower bound on the adversarial risk of downstream classifiers. Extensive experiments over several benchmarks and downstream tasks demonstrate the effectiveness and superiority of our proposed method.
Pick and Place an Object with Robot Gripper Using Arduino

Abstract: This paper presents an automatic system that picks an object from one location and places it in another location. A rover features with special type of wheels called Mecanum wheels. A pre-defined path is designed, and the rover is programmed to move the rover in the desired path. The Arduino IDE software tool is used to write the program and the program is being dumped into the microcontroller. A gripper with two arms is used to pick and place objects that are functionally similar to a hand.
The Teacher’s Approaches in Teaching English Literature Component: A Systematic Literature Review

This systematic literature review examines the teaching approaches and challenges encountered in teaching English literature in Malaysia since its inclusion in the curriculum in 2000. Twenty articles and six systematic literature reviews from 2015 to 2022 were analyzed using the PRISMA review technique. The findings reveal various approaches employed, including information-based, paraphrastic, stylistic, language-based, reader response, and moral philosophical approaches. These approaches utilize different activities to engage students in literature learning. The review also identifies common challenges faced by teachers, such as students' attitudes, limited language proficiency, inadequate teacher training, and large class sizes. By addressing these challenges, educators can create a conducive learning environment with appropriate facilities to enhance the teaching and learning process of literature. This review aims to provide valuable insights to educators in improving literature education and achieving positive learning outcomes.
Pemanfaatan Teknologi Informasi dan Komunikasi dalam Pembelajaran di SMA Bayt al-Hikmah Pasuruan

The needs and lifestyle of society in the current era have given rise to developments in various fields, one of which is technology. Changes in learning patterns are needed to carry out updates to keep up with the dynamics of increasingly rapid changes triggered by technological developments. This research uses a qualitative approach with a descriptive model to analyze and explain how information and communication technology is used in learning at SMA Bayt Al-Hikmah Pasuruan. Based on the research conducted, it can be said that the teacher paradigm when utilizing information and communication technology in learning is still limited to the use of presentations, especially power points. The use of the internet is still limited to searching for information about the material that will be presented rather than being used as a new integrated learning system, likewise social networking is still not widely used as a new learning system to further increase the effectiveness and efficiency of the learning process.
Virtual Reality images as a tool in hotel promotions: is it effective and worth investment?

This report examines how traditional photos compare to two different types of virtual reality (VR) images (static and 360°) when used as promotional tools for hotels. An experimental study by Slevitch et al.(2022), which utilized multiple types of measures, served as the foundation for the recommendations. The results showed that static VR images performed the same way as traditional photos, however the 360° VR images had a more positive impact on participants’ emotional responses and were more engaging than other images. This suggests that 360° VR images are better for hotel promotions, especially when initial impressions, such as interest and excitement are the main concern. The results also showed that using physiological measurements (i.e. fNIR, skin conductivity, and eye tracking devices) can give a more accurate picture of how people feel about something than just asking them in a survey.
Diagnosa Gejala Kecanduan Bermain Game Online dengan Metode Fuzzy Tsukamoto dan Fuzzy Sugeno

In this increasingly developing era, online games are a type of game that can provide its own pleasure for its users. Because of that, many people play online games continuously until they forget the time, it can even become addictive. Not many people have the opportunity and willingness to consult an expert to overcome their addiction. Therefore, an expert system is needed to diagnose online game addiction symptoms. With this research, an expert system was produced to diagnose symptoms of addiction to playing online games, which makes it easy to use. This expert system produces output in the form of a diagnosis whether a person is addicted to playing online games or not. Tsukamoto's fuzzy logic method is one method for a good problem solving control system to implement. The Sugeno fuzzy logic used in the expert system in this study functions to seek and find solutions to symptoms of addiction to playing online games. The results of this study can compare the Sugeno fuzzy with the Tsukamoto fuzzy to find the intended value or success by comparing the two fuzzy to find the highest value in this study. The results of this study obtained an accuracy value of 100% from 100 sample data.
Image Duplicate Detection by Using FSM Technique

In recent times, determining an image is authenticate or fabricated is big challenge. With advancement in technology an image can be tampered or forged within seconds. Detecting these kinds of forgeries has become significant issue at present. An image can be considered as important evidence but if it is forged it will be of no use. It is necessary to develop methods for differentiating between computer-generated photos and altered ones. With the view of detecting these forgeries we are going to develop Image Forgery Detection Model which will consist of RRU-Net along with FSM. RRU-Net which stands for Ringed Residual Structure and Network Architecture combines two different methodologies namely residual propagation and the residual feedback. FSM stands for Feature Similarity Module will be used to detect long-range dependencies. Combining FSM with RRU-Net to increase accuracy is our proposed system and from image patches of varying sizes, we will extract the differences in the image’s attributes between unmodified and modified sections. After detecting the forged area, the final region will be displayed in coloured form. In future, the system will be helpful to detect various spliced image forgeries that surface on the various social media platforms.
Pengembangan Media Pembelajaran Audio Visual pada Materi Menganalisis Unsur Pembangun Puisi Kelas X SMK Negeri 2 Gunungsitoli

The purpose of this study was to develop learning audio visual media on the material of analyzing the elements of poetry building class X SMK Negeri 2 Gunungsitoli. This type of research is development with the ADDIE model which consists of analysis (Analyze), design (Design), Development (Develompment), Implementation (Iplementation), Evaluation (Evaluation), the research instrument used is a validation questionnaire for material experts, linguists and design experts and a student response questionnaire. Based on the results of the study, it has been tested by material validators at revision I of 59.52%, revision II of 95.23%, language experts at revision I of 67.8% and at revision II of 100%, and design experts at revision I of 51% and revision II reached 96.7%. Furthermore, audio visuals are also very practical to use by students with a percentage of practicality in small group trials of 92.6%. And the field trial of 90% audio visual is also effective to use with the percentage of classical completeness of small group trials of 100% and field trials of 90%, From the results of the above research, the audio visual developed is feasible, practical, and effective to use in the learning process
A Novel Four-Step Algorithm for Detecting a Single Circle in Complex Images

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform.
Transformasi Adobe Flash dalam Dunia Pendidikan: Analisis Bibliometrik (2006-2023)

The use of Adobe Flash in multimedia-based learning media in the world of education is expected to improve the quality and availability of learning resources in various fields, including creating more innovative and efficient learning experiences. As a result, many studies have highlighted the use of Adobe Flash as a research focus. This bibliometric research aims to evaluate previous academic work that has been carried out in the context of the use of Adobe Flash in education. Research Method using bibliometric analysis from the Scopus Database. From the results of the analysis, it appears that there are 185 sources that review Adobe Flash in education, including articles, proceedings, and reviews. Despite fluctuations, from 2010 to 2012, there was an increase in the number of publications which then decreased from 2013 to 2017, before rising again in 2018 and peaking in 2020. The three main research groups examining the role of Adobe Flash in education come from Indonesia, with a total of 35 articles. Two top authors from Indonesia and two from Malaysia, as well as one from Turkey, topped the rankings in number of publications. O'Byrne is the researcher with the highest citations, while Wang, Susilowati, Anaraki, and Gregorius are in the top five with significant citations. In future research, considering learning process, mathematics learning, learning media, interactive multimedia, development models, and research models as a focus could be an interesting choice
A dataset of barometric readings for enhancing security and privacy of IoT

The security and privacy of wireless channels is typically enforced by leveraging cryptographic tools. However, there are scenarios where these methods are unfit, such as in resource-constrained environments, i.e., Internet of Things (IoT), or when an extra layer of security is needed. A promising solution involves correlating air pressure (barometric) readings to securely pair IoT devices while requiring zero-interaction. This paper presents an experimental dataset of real-world barometric measurements collected in open areas under different weather conditions. Specifically, our dataset includes readings recorded using the reference hardware platform BMP280. The experiments involve a reference scenario constituted by three Adafruit BMP280 barometric sensors connected to a Raspberry Pi 3 Model B board to collect barometric measurements. The three sensors represent two communicating parties (Alice and Bob) and an adversary (Eve), respectively. The dataset is constituted by three experiments characterized by different relative distances among Alice, Bob, and Eve. We considered 5cm and 2m between Alice and Bob while placing Eve at 2m and 8 meters, respectively. The second configuration, i.e., (Alice-Bob at 2m and Eve at 8m) has been replicated in a different scenario characterized by less air pressure fluctuations. The sampling frequency has been set to 70Hz while the measurements last for 50, 24 and 41 hours, respectively. Researchers can use this dataset in several ways, including: (i) Study the air pressure variation and correlation between devices separated by different distances, (ii) Develop a co-location verification extension for the Diffie-Hellman (DH) key agreement method that utilizes air pressure data streams, (iii) Study possible attacks against proximity-based authentication techniques that depend on pressure correlated variations.
Pengembangan Aplikasi Sistem Pemantauan Praktik Industri Berbasis Sinkronisasi di Sekolah Menengah Kejuruan

The research aims to: (1) analyze the application of synchronization-based industrial practice systems at SMK Muhammadiyah I Playen; (2) develop a synchronization-based industrial practice system application at SMK Muhammadiyah I Playen; and (3) test the feasibility of the developed school synchronization-based industrial practice system application. The research uses research and development (R&amp;D) methods using the codeigniter framework application. The subjects of the study included students of class XI TKJ SMK Muhammadiyah I Playen, Gunungkidul, Special Region of Yogyakarta. Data analysis uses quantitative analysis. The expected result is the application of industry practices that will be used to monitor students during industry practices based on the Codeigniter framework system. The results of the study include: (1) the PI monitoring system still uses conventional methods, monitoring guidance teachers directly come to the students to carry out PI. Students during attendance still use the presisnsi sheet held by the IDUKA supervisor, students also make activity journals on paper that has been provided by the school to fill in every day; (2) development of a PI monitoring system using the codeigniter framework application that makes it easier for guidance teachers, IDUKA supervisors and students to carry out PI implementation; and (3) a pilot of the PI monitoring system involving 56 students, five teachers, three IDUKA places is very easy to use and more effective
A hybrid attention network with convolutional neural network and transformer for underwater image restoration

The analysis and communication of underwater images are often impeded by various elements such as blur, color cast, and noise. Existing restoration methods only address specific degradation factors and struggle with complex degraded images. Furthermore, traditional convolutional neural network (CNN) based approaches may only restore local color while ignoring global features. The proposed hybrid attention network combining CNN and Transformer focuses on addressing these issues. CNN captures local features and the Transformer uses multi-head self-attention to model global relationships. The network also incorporates degraded channel attention and supervised attention mechanisms to refine relevant features and correlations. The proposed method fared better than existing methods in a variety of qualitative criteria when evaluated against the public EUVP dataset of underwater images.
FedLGAN: a method for anomaly detection and repair of hydrological telemetry data based on federated learning

The existing data repair methods primarily focus on addressing missing data issues by utilizing variational autoencoders to learn the underlying distribution and generate content that represents the missing parts, thus achieving data repair. However, this method is only applicable to data missing problems and cannot identify abnormal data. Additionally, as data privacy concerns continue to gain public attention, it poses a challenge to traditional methods. This article proposes a generative adversarial network (GAN) model based on the federated learning framework and a long short-term memory network, namely the FedLGAN model, to achieve anomaly detection and repair of hydrological telemetry data. In this model, the discriminator in the GAN structure is employed for anomaly detection, while the generator is utilized for abnormal data repair. Furthermore, to capture the temporal features of the original data, a bidirectional long short-term memory network with an attention mechanism is embedded into the GAN. The federated learning framework avoids privacy leakage of hydrological telemetry data during the training process. Experimental results based on four real hydrological telemetry devices demonstrate that the FedLGAN model can achieve anomaly detection and repair while preserving privacy.
Scalable and robust beam shaping using apodized fish-bone grating couplers

Efficient power coupling between on-chip guided and free-space optical modes requires precision spatial mode matching with apodized grating couplers. Yet, grating apodizations are often limited by the minimum feature size of the fabrication approach. This is especially challenging when small feature sizes are required to fabricate gratings at short wavelengths or to achieve weakly scattered light for large-area gratings. Here, we demonstrate a fish-bone grating coupler for precision beam shaping and the generation of millimeter-scale beams at 461 nm wavelength. Our design decouples the minimum feature size from the minimum achievable optical scattering strength, allowing smooth turn-on and continuous control of the emission. Our approach is compatible with commercial foundry photolithography and has reduced sensitivity to both the resolution and the variability of the fabrication approach compared to subwavelength meta-gratings, which often require electron beam lithography.