Reviewerly/exHarmony huggingface dataset:
Klasifikasi Performa Akademik Siswa Menggunakan Metode Decision Tree dan Naive Bayes

Getting good academic performance is the goal of the learning process carried out by the education office under the auspices of the Ministry of Education and supervised by the government. Governments that want to be successful in educating students should pay attention to their new generation because they are the future successors of the nation. Students of all levels are the benchmark for a country's success. Therefore, it is necessary to know the student's academic performance from an early age, in order to get special treatment related to the student's learning achievement. In this study, the academic achievement of students from various levels of education such as elementary, middle, and high schools was tried to be determined by applying various data mining classification methods such as Decision Tree and Naive Bayes. This data grouping is open where the data can be accessed easily and can be used in future research. The data grouping is divided into 3 categories, namely Low (L), Medium (M) and High (H). From the results of the dataset trial, it shows that the highest classification accuracy is Decision Tree of 83.89% and Naive Bayes of 85.97%. Thus the Naïve Bayes method is more accurate in grouping students' academic performance data. The results of this study will be used to create a student grouping system based on student learning performance using the appropriate algorithm. So that his contribution later when creating the application system for the formation of study groups can use the Naïve Bayes method.
One-to-many Image super resolution algorithm based on generative adversarial networks

Aiming at the issues that the current Generative Adversarial Network (GAN) used for image super-resolution reconstruction is easy to produce false details and the mapping dependence is not flexible enough, a One-to-many Generative Adversarial Network (OTMGAN) based on Enhanced Super Resolution Generative Adversarial Network (ESRGAN) was proposed. First, the model uses one-to-many constraints to allow the estimated patches to dynamically seek the best supervision during training, instead of the one-to-one constraint from low-resolution (LR) images to high-resolution (HR) images, thus improving the loss function of the model. Then, a new region-aware adversarial learning strategy is used to distinguish between flat regions and textured regions, so that the model can adaptively generate more realistic details for textured regions during training. Finally, the experimental consequences of image super resolution reestablishment on the public datasets BSDS100, Set5, Set14 and DIV2K show that the new model is significantly superior to the classical models such as ESRGAN in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learning Perception Image Patch Similarity (LPIPS).
THE PRACTICAL RESEARCH OF MIND MAPPING IN THE MATHEMATICS BISECTION CLASSROOM OF JUNIOR MIDDLE SCHOOL

With the continuous reform of education, the focus of teaching has shifted from teachers' teaching to students' learning. In the divided classroom, students' learning autonomy and learning ability can be improved through students' internalization and absorption, discussion in separate classrooms and other links. Mind mapping can link knowledge points and help students clarify knowledge structure. This paper takes "review at the end of the chapter of unitary linear equation" as an example to explore the integration of split classroom and mind mapping in junior high school mathematics, optimize the teaching process, and establish an efficient classroom.
On Prof. Farish’s Immediately Comprehensible and Cezanne’s Immediately Puzzling Pictures

Professor William Farish, professor of chemistry concerned with engineering at the University of Cambridge, had a problem.
Assessment of E-learning Technologies in Universities: Case of University of Dar Es Salaam

The universities worldwide are struggling to use e-learning technologies to solve the current situation created by pandemics. The universities in Tanzania found themselves with no option but to make use of e-learning like any other public university in the world to support students away from them. There are various e-learning technologies, and according to the various studies reviewed, universities use an amalgamation of e-learning technologies to support the teaching and learning processes. These technologies disorient the users when they want to integrate them during the teaching and learning process. The study adopted a case study design based on the University of Dar es Salaam. The study applied stratified sampling techniques to group the students based on the year of study and simple random sampling was used to get a sample of 392 based on the strata. A questionnaire was used as the tool to get data from the respondents. The findings of this paper will be essential in identifying the most effective e-learning technologies based on the preference to support the learning process.
The Application of Task-based Teaching Method in College English Teaching

The main purpose of a university English course is to develop student's English language skills. However, in the traditional English teaching mode, the teacher is the centre of teaching, and it is difficult for students to participate in classroom activities. And the phenomenon of over-emphasis on English reading materials in the teaching process leads to limitations in students' understanding of the content of English texts, which not only affects the quality of university English courses but also affects students' reading speed. This paper focuses on the task-based teaching method and discusses its specific application in university English teaching.
Frame Interpolation for Weather Radar Data

Rain and snow contain particles generated by different physical and chemical processes. Weather radars send directional pulses of microwave radiation, on the order of a microsecond long. Between each pulse, the radar station serves as a receiver as it listens for return signals from particles in the air. This is the measurement principle of microwave weather (meteorological) radar. Limited by the information processing capability, the weather radar data published by the meteorological website often have a large time interval, such as 6 min. Video frame interpolation technology has made great progress in recent years with the development of deep learning technology. The frame interpolation of weather radar charts will bring users more accurate weather descriptions and more intuitive decision-making references. This paper propose a novel video frame interpolation algorithm for weather radar data, which is able to generate the intermediate frames between the frames at the sampling time.
Contribution of Ghana’s School Feeding Programme On Enrollment and Academic Performance of Public Basic Schools. A Case of Students in West Mamprusi Municipality

The study's goal was to determine how the Ghana School Feeding Programme affected basic school education. The study primarily examined the impact of the Ghana School Feeding Programme on the enrollment and academic performance of students in basic school. Mixed methods approach was used in the examination of some selected basic schools in Ghana's North Eastern Region, West Mamprusi Municipality that were participating in the Ghana School Feeding Programme. For the purpose of this study, teachers, head teachers and students were involved. 2,028 students formed the study population, and 200 students were chosen at random to represent the sample size. To create the sample size, twelve (12) teachers/head teachers in all were purposively chosen. The major research instrument used was questionnaire, supported by interview. The reliability of the instruments was evaluated using the tests-retest approach. The test-retest correlation coefficient demonstrated the validity and reliability of the questionnaires. The study discovered that the Ghana School Feeding Programme was a crucial and important factor determining enrollment and academic performance of students in basic education in the study schools. The study's findings indicate that the Ghana School Feeding Programme is still vital to the achievement of fundamental education and that every effort should be made to broaden its structures and purview for the good of all schools. Consequently, it is advised that the implementation committees at the Municipality should continue to monitor the quality and quantity of the daily meals provided for students in order to reduce hunger and enhance enrollment, concentration and performance in classrooms. Meals must also be nutritionally balanced and provided on time.
Cluster-based denoising autoencoders for rate prediction recommender systems

Recommender system (RS) is a suitable tool for filtering out items and providing the most relevant and suitable items to each user, based on their individual preferences. Deep learning algorithms achieve great success in several fields including RS. The issue with deep learning-based RS models is that, they ignore the differences of users’ preferences, and they build a model based on all the users’ rates. This paper proposed an optimized clustering-based denoising autoencoder model (OCB-DAE) which trains multiple models instead of one, based on users’ preferences using k-means algorithm combined with a nature-inspired algorithm (NIA) such as artificial fish swarm algorithm to determine the optimal initial centroids to cluster the users based on their similar preferences, and each cluster trains its own denoising autoencoder (DAE) model. The results proved that combining NIA with k-means gives better clustering results comparing with using k-means alone. OCB-DAE was trained and tested with MovieLens 1M dataset where 80% of it is used for training and 20% for testing. Root mean squared error (RMSE) score was used to evaluate the performance of the proposed model which was 0.618. It outperformed the other models that use autoencoder and denoising autoencoder without clustering with 38.5% and 29.5% respectively.
The development of employee attendance application based on mobile using spiral method

The Pangulu Nagori Amborokan Panei Raya Office is one of the governmental institutions tasked with serving the local population. The Pangulu Nagori Amborokan Panei Raya office still records employee attendance using the manual approach. Employee attendance is noted in the attendance book. When done this way, there are still issues that come up. Particularly the time and effort put forth to complete the attendance procedure. In addition, because attendance data has very little protection and is accessible to anyone, it is challenging to summarize staff attendance reports. Building an Android-based application that can be used to process and track employee attendance is the aim of the author's research. The author employs UML to visually create the application design (Unified Modeling Language). The system development methodology, however, is a spiral that includes the phases of customer evaluation, planning, risk analysis, engineering, building, and disposal. The Kodular framework and the Airtable database are used in the development of an Android-based attendance application. The results of the study are an android-based attendance application that can be applied to make it easier for employees to take attendance and monitor employee attendance in real-time using an Android smartphone.
A novel hybrid feature extraction and ensemble C3D classification for anomaly detection in surveillance videos

Anomaly detection in several deep learning frameworks are recently presented on real-time video databases as a challenging task. However, these frameworks have high false positive rate (FPR) and error rate due to various backgrounds, motion appearance and semantic high-level and low-level features for anomaly detection through action classification. Also, extraction of features and classification are the major problems in traditional convolution neural network (CNN) on real-time video databases. The proposed work is a novel action classification framework which is designed and implemented on large video databases with high true positive rate (TPR) and error rate. In this framework, Kalman based incremental principal component analysis (IPCA) feature extraction method; C3D and non-linear support vector machine (SVM) classifier are used to improve the action prediction (anomaly detection) on the large real-time video databases. The proposed frame work shown new results of high computation performance than the traditional deep learning frameworks for action classification.
The Impact of Using Voice of America (VOA) in Teaching Degree Comparison to Indonesian Junior High School Students

This research was conducted based on the main problem is the learning effect of using the voice of America (VOA) YouTube Channel in Degree of Comparison. Based on the main problem formulated above, the research objectives are as follows: to determine the effect of using Voice of America (VOA) learning english YouTube Channel in teaching degree of comparison in the eighth grade of SMPN 24 Kota Serang. The subject of this study involved 60 students as a sample from class VIII SMPN 24 Kota Serang in 2022 as a population. The show that the value of t0 is 3.26 with degrees of freedom (df) is 58. There was a significant difference between students' understandings on degree of comparison Using Voice of America (VOA) English learning through YouTube Channel. The study found that most of the data showed an increase in learning using YouTube video channels. It is suggesting that students should consider the learning method used to achieve sustainability.
Exam Questions from Essay to MCQ with Different Levels of Difficulty in Engineering Mechanics

In this paper some techniques to avoid students’ guessing of the correct answer in MCQ exams are proposed. An example from engineering mechanics course is introduced with different levels of difficulty. Random and adaptive selections of choices are introduced according to the physics and difficulty of the problem. The proposed techniques proved to be efficient in reducing guessing of correct answers based on partial or no knowledge of the subject matter. Also, a proposal of establishing online exam using pools of MCQ’s with different levels of difficulty is introduced. Five basic types of exams with different number of questions based on instructors’ desire are tabulated.
Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning

Solar energy will be a great alternative to fossil fuels since it is clean and renewable. The photovoltaic (PV) mechanism produces sunbeams’ green energy without noise or pollution. The PV mechanism seems simple, seldom malfunctioning, and easy to install. PV energy productivity significantly contributes to smart grids through many small PV mechanisms. Precise solar radiation (SR) prediction could substantially reduce the impact and cost relating to the advancement of solar energy. In recent times, several SR predictive mechanism was formulated, namely artificial neural network (ANN), autoregressive moving average, and support vector machine (SVM). Therefore, this article develops an optimal Modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction (OMBGRU-SRP) for energy management. The presented OMBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process. To accomplish this, the presented OMBGRU-SRP technique performs data preprocessing to normalize the solar data. Next, the MBGRU model is derived using BGRU with an attention mechanism and skip connections. At last, the hyperparameter tuning of the MBGRU model is carried out using the satin bowerbird optimization (SBO) algorithm to attain maximum prediction with minimum error values. The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild. Many experiments were conducted to demonstrate the enhanced SR prediction performance. The experimental values highlighted the supremacy of the OMBGRU-SRP algorithm over other existing models.
SUPPORT CENTER FOR MANAGEMENT AND PROMOTION IN SERVICE OF EUROPEAN RESEARCH PROJECTS

The Remote Sensing department from National Institute of R&amp;D for Optoelectronics INOE, Romania, operates a complex research infrastructure and has multidisciplinary expertise spanning from laboratory/in-situ chemical analysis to Earth Observation (EO) techniques and applications. Infrastructure and personnel are mostly supported from projects financed by European Commission through FP7/Horizon 2020 (ended programs) and Horizon Europe (current programme) and other international calls (e.g., European Space Agency). In order to facilitate support of R&amp;D projects submission/execution/reporting to Horizon Europe framework program competitions and other international calls, INOE is building a dedicated center until mid-2023, through PREPARE project - -Support Center for European Project Management and European Promotion-, funded under the Competitiveness Operational Program (2014- 2020), the action 1.1.3 "Creating synergies with the RDI actions of the European Union's HORIZON 2020 framework program and other international RDI programs". This work presents the results of our journey of drawing the main components of the management and promotion center that will mainly support large projects related to ACTRIS - Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS). The center is intended to guide and support scientists- approach from idea to grant negotiation stages and also to project implementation and reporting.
An Exploratory Study on Students’ Electricity Consumption in RCE Gombak For Sustainable Communities

Rising energy demand in Malaysia has become an increasing concern. Malaysia uses more energy to produce a unit of GDP over time; when more energy is needed to create an output of similar value, this impacts the overall cost of production and may contribute to increased environmental degradation. In line with SDG 11: Sustainable Cities and Communities and SDG 12: Responsible Consumption and Production, this study aims to understand the electricity consumption of students and their knowledge of energy efficiency. The study was conducted at Sekolah Berasrama Penuh Integrasi Gombak in the year 2022. A questionnaire was distributed to the students containing questions about basic knowledge of climate change and energy efficiency. The survey also contained questions to gauge the students’ electricity consumption behaviour. Some of the questions were formulated based on Malaysia’s National Energy Efficiency Action Plan 2016-2025. The purpose is to discover to what extent secondary school students are aware of government initiatives to reduce energy consumption.
Financial Perspective Thought Experiment on Russian Cyber Threat Actors

Due to the advancement of information and communication technology and related services, the digital world has reached many people, private companies, and governments, and meanwhile, threat actors regarding motivation, knowledge, and capabilities have also evolved, and thus, today, they compete and collaborate with others. Financially motivated threat actors also do businesses; as such, with a higher sophistication level, they create tools and provide them as Malware as a Service (MaaS) for renting, and if they can extract accounts, they launder those amounts of cash through hardly traceable channels. In contrast, state-sponsored threat actors act according to the government&amp;rsquo;s political and military needs.&#x0D; &#x0D; The Russian government lets independent threat actors freely conduct various cyberattacks, including cyber espionage, sabotage, and ransomware attacks on non-Russian geolocations and entities, meanwhile financing its threat actors to achieve social and political activities. As such, providing a thought experiment, the paper examines the potential income of a for-profit organization, the related tax income, and the costs of operating a government-related threat actor. To conduct the analysis, it provides a methodological approach and applies that to TA542 and APT28 threat actors, using inputs from open-source intelligence.
Persepsi Pengalaman Pengunjung Pameran terhadap Teknologi Self Service Registration

The increasing enthusiasm of visitors at exhibitions has had an impact that cannot be managed properly in the registration section. Thus making the professional exhibition organizer (PEO) as the event organizer present new innovations in managing visitor registration before entering the exhibition area. This innovation is self-service registration, by means of self-serving by the visitors themselves. That way it provides good two-sided benefits, namely for the PEO and the visitors who attend. Using a descriptive descriptive research approach with the method of observation, interviews, and literature study. Producing research findings that there is ease in the process of collecting visitor data and providing effectiveness for visitors to take advantage of their own gadgets. However, there are also problems when the server is down and when the internet connection is not connected, so that it has a direct impact on PEO to continue to be adaptive in developing this self-service innovation.&#x0D; Keyword: Events, Registration, Perception, Exhibition.
KLASIFIKASI JUMLAH KENDARAAN DI SUMATERA UTARA DAN SUMATERA BARAT MENGGUNAKAN ALGORITMA NAIVE BAYES

Abstract : The Central Bureau of Statistics (BPS) is a non-departmental government agency established as a provider of data or information based on Law No. 6/1960 on Census and Law No. 7/1997 on Statistics. The Central Statistics Agency (BPS) recorded the number of motorized vehicles such as cars, buses, trucks, and motorcycles in the provinces of North Sumatra and West Sumatra in 2020-2021 reaching 3,043,892 million units. The purpose of this study is to classify the number of motorized vehicles in the form of cars, motorcycles, buses and trucks. This research uses quantitative research with Naive Bayes Algorithm analysis model. The data used in this study is data from several regions in North Sumatra Province and West Sumatra Province in 2020-2021. Evaluation of model performance is based on accuracy parameters, precision and total recall of the confusion-matrix. The results of testing the dataset and calculating the model performance parameters have obtained an accuracy value of 100%. With the percentage value of the description of each dataset class, namely a little 70.6%, moderate 21.6%, and a lot 7.8%. Keywords :classification;confusion-matrix; data; motor vehicles; naive bayes Abstrak : Badan Pusat Statistik (BPS) adalah lembaga pemerintahan non-departemen yang dibentuk sebagai penyedia data atau informasi berdasarkan UU Nomor 6 Tahun 1960 tentang Sensus dan UU Nomor 7 Tahun 1997 tentang Statistik. Badan Pusat Statistik (BPS) mencatat jumlah kendaraan bermotor seperti mobil, bus, truk, dan sepeda motor di Provinsi Sumatera Utara dan Sumatera Barat pada tahun 2020-2021 mencapai 3.043.892 juta unit. Tujuan dari penelitian ini yaitu untuk mengklasifikasikan jumlah kendaraan bermotor berupa mobil, sepeda motor, bus dan, truk. Adapun penelitian ini menggunakan jenis penelitian kuantitatif dengan model analisis Algoritma Naive Bayes. Data yang digunakan pada penelitian ini adalah data dari beberapa daerah di Provinsi Sumatera Utara dan Provinsi Sumatera Barat tahun 2020-2021. Evaluasi kinerja model didasarkan pada parameter akurasi, presisi dan recall total dari Confusion-Matrix. Hasil pengujian dataset dan perhitungan parameter performa model telah didapat nilai akurasi 100%. Dengan presentase nilai keterangan setiap kelas dataset yaitu Sedikit 70,6%, Sedang 21,6%, dan Banyak 7,8%. Kata kunci :confusion-matrix; data; kendaraan bermotorklasifikasi; naive bayes