Reviewerly/exHarmony huggingface dataset:
Pengaruh Media Powtoon dalam Mata Pelajaran Pendidikan Agama Islam terhadap Hasil Belajar Siswa Kelas VII

The lack of media use and the subpar academic performance of seventh graders at SMP Negeri 2 Bukittinggi are the driving forces for this study. 1) The primary objective of this study is to identify the learning process using Powtoon as a medium. 2) to assess educational results utilizing Powtoon media 3) to assess the effect of Powtoon media on seventh-grade students' academic achievement at SMP Negeri 2 Bukittinggi. Experimental quantitative research is used in this work. Students from SMP Negeri 2 Bukittinggi's classes VII.1 and VII.2 made up the study's population. Purposive sampling was used as the sample method in this study. Tests of student learning outcomes, surveys, and documentation were used to gather data. The findings of the study were evaluated using the lndependent Sample T-Test. based on the research's conclusions from a survey 1) The average (Mean) usage of Powtoon media was 101.13, and 35% of students fell into the adequate category. 2) Up to 20% of pupils fall into the sufficient category for learning outcomes, as shown by an average score of 83.26. 3) The R square test result of 0.643 (64.3%) which demonstrates a relationship between the independent and dependent variables demonstrates that Powtoon media has an impact on class VII students at SMP Negeri 2 Bukittinggi's learning outcomes.
Editors' Introduction

In this introduction, the editors continue their reflections on scholarly editing in the pandemic, welcome two new co-editors, and announce a grant-funded initiative that builds on Lateral Forums. This issue features three regular articles and the first installment of a special section, Crip Pandemic Life: A Tapestry, which builds on the 'Cripistemologies of Crisis' special section, edited by Theodora Danylevich and Aly Patsavas, last year.
The Use of Affixed Words in BIPA Student Writing Beginner Class

Indonesian is a second language for BIPA students and should be learned after the primary language. This leads to differences in the understanding of linguistic structures focused on the order of affixes. This research aims to describe the use of affixed words and the errors of words with affixes in the essays of beginner class BIPA students at the University of Muhammadiyah Malang. The method used in this research is a qualitative method with a descriptive type of research. The source of the data used is a narrative text compiled by students of BIPA University of Muhammadiyah Malang for beginner class. The data used in this research are words with affixes with both appropriate and inappropriate use in BIPA students’ essays. In this research, reading and note-taking techniques were used for data collection. The technique used to analyze the data in this research is replacement technique. Based on the result of the study, it can be concluded that there are three forms of affixes in BIPA students’ essays, including 118 data prefixes, 66 data confixes, and 38 data suffixes. In this case, three types of affixation errors were also found, including the omission of affixes as much as 7 data, replacement of morphs as much as 2 data, and the use of incorrect affixes as many as 26 data. This research can be used as a reference for further research, and as information for readers, especially BIPA students and teachers, to use correct Indonesian grammar.
Enkripsi dan Dekripsi Pesan Menggunakan Polinomial Galois Field dengan Algoritma Hill Cipher

An information is extremely easy to obtain when technology is evolving so fast and it is important to secure the information to avoid irresponsible parties who want to abuse it. Messages that are not safe will harm the owner of the message, therefore a lot of research is related to message security. This study aims to deepen the message security in encoded form using Galois Field polynomials with one of the symmetric encryption algorithms, namely the Hill Cipher algorithm. Encryption is the process of converting a message into a secret code while decryption is the reverse process. The encryption and decryption process in this discussion is carried out by changing the message character into Galois Field polynomial form, then operating calculations based on the Hill Cipher algorithm, and then changing the results of the calculation operations into character form again. Based on the modifications have been made, the encryption and decryption formulas will be multiplied by modulo in polynomials form. In this study, the encryption and decryption keys are in matrix form with elements in it are members of the Galois Field polynomial and the result is a random message of 256 characters in binary bit-8. From this research, we gain insight into the encoding that can be done using polynomials which can be used later in other forms of encoding.
Fine Tuning Transformer Based BERT Model for Generating the Automatic Book Summary

Major text summarization research is mainly focusing on summarizing short documents and very few works is witnessed for long document summarization. Additionally, extractive summarization is more addressed as compared with abstractive summarization. Abstractive summarization, unlike extractive summarization, does not only copy essential words from the original text but requires paraphrasing to get close to human generated summary. The machine learning, deep learning models are adapted to contemporary pre-trained models like transformers. Transformer based Language models gaining a lot of attention because of self-supervised training while fine-tuning for Natural Language Processing (NLP) downstream task like text summarization. The proposed work is an attempt to investigate the use of transformers for abstraction. The proposed work is tested for book especially as a long document for evaluating the performance of the model.
Interactive visualization of forearm internal rotation angle in Tai Chi training

Modeling the visual features of the forearm internal rotation angle during Tai Chi training is difficult, and its accurate detection is lacking. To address this issue, this paper proposes an interactive visualization analysis method based on Harris corner detection and edge contour feature extraction to visualize and adjust the internal rotation of the forearm during Tai Chi training in real time. In the proposed method, the visual images of limb movement and forearm rotation during the Tai Chi training are collected as features, and the wavelet image denoising method is used to denoise the images to improve the signal-to-noise ratio of the output. Meanwhile, the image pixel uniform traversal method is used to continuously traverse the image subblocks to extract the edge contour features during the forearm internal rotation interaction. By means of superpixel feature decomposition and grayscale information superposition, the fitting effect of the forearm internal rotation angle is fed back, and based on this, the forearm internal rotation angle is modified to realize the interactive visual analysis of the forearm internal rotation angle. Experimental results show that, compared with existing methods, the interactive visualization performance of the proposed method is better. The proposed method can fit, track, and solve the rotation angle in real time; the required analysis training images are more accurate, and the Tai Chi training data detection and dynamic analysis abilities are improved.
The attitudes of Jordanian English language and literature undergraduate students toward open-book exams

Introduction The paper aims to investigate the attitudes of English Language and Literature students toward implementing the open-book exam in literature courses. Methods The research applied a mixed-method approach. The researchers interviewed five undergraduate students enrolled in the American Literature course for the qualitative part. In contrast, the research includes the results of 62 questionnaires for the quantitative part. Moreover, a survey questionnaire was developed by the researchers to collect data concerning the participants’ attitudes toward OBEs. Results The study found a positive attitude toward OBEs. Moreover, the study participants demonstrated that OBEs reduce stress levels that occur during exams in general. A statistical representation of the findings showed that the sample students have a positive attitude in relation to this unconventional assessment protocol. Discussion OBEs should be allowed more space in academic institutions since today’s world does not depend on the amount of information a job applicant deposits in his memory. What matters is the person possesses enough high-order thinking abilities and skills like problem-solving.
Secure Cloud Data Storage with Selecting Auditors

Outsourcing data is out of the physical control of users, and its integrity verification is an important means to ensure data security. Reviewing of the problem that auditors are fixed in the existing public audit schemes, an integrity verification scheme in which users can choose auditors was proposeed, so that users can choose appropriate auditors according to their own actual situation and make reasonable use of resources. Furthermore, it can be extended to select double auditors for integrity verification. There is no interaction between the two auditors, which can improve the verification credibility. The safety analysis and efficiency analysis show that the scheme is safe and efficient.
Sentiment and complexity analysis on two databases in Bulgarian language – final estimation

The objective of this paper is to explore in depth and find the reason that lies behind the difference in the results that two databases with user reviews in Bulgarian language give after the application of algorithms like Logistic Regression, Support Vector Machines and Naïve Bayes, as well as Random Forest and Recurrent Neural Network. In the author's previous works were created two databases with customers' comments in Bulgarian language and after that were applied the stated algorithms. The purpose of the current project is to find the reason for the different final accuracy results the two databases give, by making a complexity analysis of the language used in them. In addition, the authors aim to find the most accurate model for complexity analysis in Bulgarian language, given the specifics of the language.
APLIKASI PREDIKSI DALAM PENENTUAN JUMLAH PRODUKSI AIR MINUM ADENI PAMEKASAN DENGAN MENGGUNAKAN METODE FUZZY MAMDANI

Uncertainty was a problem that can be found in the daily life, including when make a decision. In case when trying to determine the amount of production number, fuzzy logic concept is very effective because it can solves problem that contain uncertainty, partial truth and ambiguous. One of the fuzzy method that can be use to solve that problem was Fuzzy Inference System Method. Fuzzy Inference System Method that being used in this minithesis is Fuzzy Mamdani Method and software that being used is Visual Basic 6.0 with Microsoft Acces database. The results from Fuzzy Mamdani Method implementation will give a solution about the optimal amount that need to produced.Key Word : Fuzzy Inference Sistem, Mamdani Method, Determining Production Amount.
Challenges to Democracy: Attitudes Towards the January 6 Events at the Capitol on Social Media

On January 6, 2021, a group of protesters contesting the results of the 2020 US Presidential election laid siege to the US Capitol Building in Washington, D.C., resulting in several deaths, hundreds of injuries, property damage, the temporary interruption of the certification of Electoral College votes by Congress, and, ultimately, in the second impeachment of former US President Donald Trump. Users on social media platforms have reacted by posting messages expressing their feelings towards these events, supporting or rejecting the actions of the protesters. The present paper aims to analyze the opinions the public posted on social media with respect to the incident, and their evolution over time. For this purpose, we gathered a dataset containing 50,618 English-language tweets posted during two periods: January 2021, when the original events took place, and January 2022, when the incident’s first anniversary prompted renewed interest on social media and among wider society. We train several machine learning and deep learning algorithms and use the best-performing model, RoBERTa, to carry out stance analysis on the dataset, classifying each tweet as neutral, in favor, or against the actions of the Capitol rioters. We then use the NRCLex emotion lexicon to identify emotions expressed for each class, comparing the evolution of opinions over the two periods. The results we present can be useful for social media stakeholders, government regulators, and the general public in order to better understand the relationship between social media communication and the political system, as well as highlight the threat posed to democracy by increasing political partisanship and polarization.
Classifying Students’ Academic Performance Using Principal Component Analysis During Pandemic COVID-19

As a result of the COVID-19 outbreak in Malaysia, educational institutions, including universities, were forced to conduct all academic activities online. If online learning continues in subsequent academic sessions, it may have a big impact on the academic performance of the student. Thus, the purpose of this study is to classify student’s academic performance by using principal component analysis (PCA) before and during pandemic COVID-19. The sample consisted of 234 undergraduates from the Department of Mathematics at the Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Data collection is conducted using a questionnaire. Data were gathered from students’ grades for each subject in the first semester of the session 2019/2020, which is during face-to-face learning, and each subject’s grade in the second semester of the session 2019/2020, which is during online learning. The finding of the study was divided into two parts which are preliminary analysis and further analysis. According to the eigenvalues greater than one criteria (Kaisar, 1960), when using the correlation matrix in eigen analysis, only, values greater than one should be included in the analysis. As a result, the researcher decided to cut-off the dimension at the second dimension from the eigenvalues obtained by the R software because the eigenvalues for the second dimension were greater than one which is 1.111. For further analysis, the researcher will be using the principal component one in classifying students based on their overall academic achievement, and principal component two will be used in classifying in which semester they did best in. With regard to overall academic performance, the majority of students’ 51.4% were in the classification of good performance, while just 24.3% were considered excellent performance. Further, 15.7% of students are categorized as performing averagely in their academic performance, whereas 8.6% are categorized as performing below average. The results indicate that 65.7% of students performed similarly in both semesters, indicating that most students are performing in their academic performance either before or during the pandemic COVID-19, while only 14.3% of students performed in semester 1 session 2019/2020, which is during face-to-face learning, and only, 20% of students performed in semester 2 session 2019/2020, which is during online learning.
Leveraging the Goldfinger Attack in Blockchain Based on the Topological Properties

In this paper, we provide a new approach to modeling and analyzing the Goldfinger attacks in blockchain networks, based on the topology of a peer-to-peer network; this paper studied the impact of the Goldfinger well known as 51% attacks in the case of the ring, mesh and fully connected topology. The outcome of simulation has proven that in the case of fully connected topology, the attacker node with 501 (hash/s) has found 155 blocks in 60 s and the rest of the nodes, on average, 58 per node; this gives the control of the balance to the blocks of node J with 131 balance and 43 balance on average for nine nodes, in practice, the node with more than 50% will monopolize the balance. The Goldfinger occurred very worst in the fully connected topology and monopolized the network because of topologically connections; the attacker in fully connected topology is a direct and duplicated connection with the nine nodes. In terms of connection duplicated bidirectional or directional, the fully connected topology presents a hard fork compared to ring and mesh topology after the attack.
A Case Study in the Application of STPA-sec and CHASSIS for Socio-Technical Cyber Security Risk Management in Health Care from Developing Nations

Healthcare organizations continue to face an increasing range of cyber threats. In developing nations, considered capabilities of healthcare systems including fragmented governance, limited resources and experience; it is important to identify methods that can be beneficial in the socio-technical analysis of breaches given that at the moment the level of detail and the socio-technical focus varies between incidents—some reports do not mention socio-technical issues at all—and without consistent methods we cannot be sure whether this was due to the fact socio-technical factors were not important or whether they were not just considered by the analyst. We address these problems by evaluating the application of two promising approaches STPA-sec (System Theoretic Process Analysis) for cyber security and a focused method for use case and misuse case scenarios, CHASSIS (Combined Harm Analysis for Safety and Security of Information System). STPA-sec has been able to capture ranges of socio-technical risks threatening cyber security of picture archiving and communication system (PACS) in this hospital by making rapid integration in a resource-constraint area. However, our results show that given a variety of socio-technical risks in this area—interaction of fragmented components, organizational vulnerabilities, it can be hard for only a high-level and abstract methodology; hence, it is needed to identify other forms of breaches to the analyzed system; for that reason, CHASSIS is used to support STPA-sec analysis; this provides to the local analyst another point of consideration in handling different socio-technical risks.
A Channel-Based Attention Deep Semantic Segmentation Model for the Extraction of Multi-type Solar Photovoltaic Arrays from Large-Scale Orthorectified UAV RGB Imagery

The extraction of photovoltaic (PV) arrays from remotely sensed aerial imagery is the core element in the mapping of PV sites. Moreover, it is of high utility in increasing the robustness of automatic imagery-based inspection models through reducing their searching space. The extraction can also be used reliably as a tool in assessing the generation capacity of PV installations. In this context, we leverage the channel-attention deep learning mechanism to exploit the full potential of the RGB spectrum and develop a deep semantic segmentation model for the extraction of PV arrays from very high spatial resolution imagery acquired by unmanned aerial vehicles (UAVs). We collect aerial images from four large-scale PV sites featuring multi-type PV arrays in various arrangements and backgrounds. After a photogrammetric post-acquisition workflow, we prepare a labeled big imagery dataset comprising 7319 unique 256 × 256 px tiles with a spatial resolution around 1 cm/px. Our final model relies on a hybrid LinkNet architecture embedding a squeeze-and-excitation (SE) 18-layer residual network backbone. It operates on monocrystalline, polycrystalline and thin-film surfaces, on which it achieves the state-of-the-art performance, with a mean IoU of 86.67% and F1-score of 92.20% on our test set.
Modeling the Prices Variation of Agricultural Products on the Stock Market Using Evolutionary Approach

The paper proposes a new method to simulate the variation of the prices of agricultural products on the stock market, using a co-web evolutionary adaptive model of concurrent sales strategies.
Evaluation of Single-Stage, Two-Stage, and Anchor-Free Object Recognition Methods for UAV Detection

Drones and Unmanned Aerial Vehicles (UAVs) are one of the cheapest and most difficult to counter aerial vehicles inducing security and safety concerns in many sectors. Recent events suggest that traditional detection technologies such as radar are not effective for detecting these vehicles despite the huge investments from countries suffering from various drone attacks. Current technologies often rely on data such as images to detect and locate drones in the sky using advanced AI-based approaches. Given these challenges, the global aim of this research is to evaluate the effectiveness of various deep learning-based object detection methods on the task of visually detecting airborne drones from camera feeds. We mostly compare single-stage, two-stage, and anchor-free object recognition methods in terms of recognition performance and speed. As per the data, we use as a common benchmark; the Anti-UAV dataset presented at the ICCV 2021 conference. From the results, we see that anchor-free models perform better compared to the other methods.
Breast Cancer Classification Using Improved Fuzzy C-Means Algorithm

Abnormal growth in the breast tissue prompts to the strange cell development in the breast. To decipher this statement in a mammogram precisely, the quality of the pictures ought to be at its incomparable. The proposed research work is conveyed out for examinations of different screening strategies to recognize the unique phases of breast malignancy. In India for every 4 min, the women are diagnosed with this disease. And a woman dies with this disease for every 13 min. This disease is prominent with the people living in the ruler area while comparing the people in the urban areas. Therefore, it is very important to find and treat this disease as early as possible. The breast tumor region, perimeter and breadth are assessed from mammogram picture databases. The Bits Errors Degree (BER), Highest Indication to Clatter Percentage (PSNR) and Callous Tetragonal Inaccuracy (MSE) values are determined for both abnormal and normal images. These analyses were used to approve the presence or absence of the disease and to support the evaluation process for finding the disease. This quality assessment is used to understand the reality on Earth for a specific diagnosis that is a specific type of chromatin in a carcinogenic core that may indicate an irregular protein sequence.
Applications that Use Recurrent Neural Networks and Their Impact on People’s Lives

Human intelligence has no limits and constantly impresses with innovative solutions that increase in complexity, applicability, and efficiency. Over time, a lot of smart applications have been substantially improved by including artificial neural networks. Thus, they came to perform tasks that only humans could accomplish. Other smart applications made decades ago are the starting point for other more complex smart solutions in which researchers have invested time, money, courage, patience, desire for progress and knowledge, and research. In this paper we highlighted the importance of using recurrent neural networks, we exemplified the use of recurring neural networks in two applications in different fields of activity, and we listed the benefits that can be obtained by using applications, the limits of artificial neural networks, the need for human intervention to obtain desired results. My work is inspired by the recent development in neural translation using the attention mechanism in order to learn long sequences and improve the performance of translation from one language to another. Using this approach, I set out to provide arguments to highlight the effectiveness of implementing solutions based on artificial neural networks, but also the fact that in all intelligent solutions based on this network and which have been created so far, the human factor is indispensable.