Reviewerly/exHarmony huggingface dataset:
Computer Vision Application in Object Detection and Tracking for Aerial Surveillance

Objectives: Computer vision duties like object detection, tracking, and counting are significant for surveillance. Factors like altitude, camera angle, occlusion, and motion blur make it a more challenging task. To present a method to overcome all these factors and implement surveillance quickly and accurately for smaller and larger object aspect ratios. Methods: Horizontal Bounding Boxes and Oriented Bounding Boxes (HBB and OBB) are evaluated on two ground truths respectively. PASCAL VOC 07 metric is adopted to calculate the mean average precision. Constructed on the score, the original implementation of Mask R-CNN includes the application of a mask head to the highest-scoring 100 HBBs. Subsequently, the mask head was extended to all HBBs remaining after the process of Non-Maximum Suppression. This modification allowed the evaluation of Mask R-CNN, Cascade Mask RCNN, and Hybrid Task Cascade methods on a wider range of bounding boxes. Findings: In summary, this research explores and compares different approaches and techniques in the field of object detection, particularly focusing on oriented object detection and the challenges posed by geometric variations. Furthermore, it addresses the impact of different models, such as Mask R-CNN, Faster R-CNN OBB + RoI Transformer, and Faster R-CNN OBB + Dpool, on performance. Additionally, it highlights the importance of handling numerical instability caused by extremely small instances. The research findings are visually presented in Figure 2, providing a clear representation of the performance of various networks. Novelty: The study summarizes the findings of existing research papers and identifies research gaps. The performance parameters of the various algorithms and analysis for various networks show the evolution of various methods over the years. With changes in the network, like mask transferring and dataset, the accuracy for smaller, bigger objects and speed of execution are affected, are explained in results and discussions as well as the conclusions. Keywords: RCNN; Deep learning; Object detection; Computer Vision; Drones
Analisis Efek Augmentasi Dataset dan Fine Tune pada Algoritma Pre-Trained Convolutional Neural Network (CNN)

Kemajuan teknologi deep learning seringkali berbanding lurus dengan keterkaitan metode yang dapat diandalkan dalam penggunaan jumlah data yang besar. Convolutional Neural Network (CNN) adalah salah satu algoritma deep learning yang paling popular saat ini guna pengolahan citra. Pada era deep learning model CNN yang kompleks seperti saat ini memiliki tantangan-tantangan yang baru baik gradient vanishing, overfitting yang dikarenakan keterbatasan dataset, optimasi parameter hingga keterbatasan perangkat keras. Penelitian ini bertujuan mengukur pengaruh teknik fine tuning dan augmentasi dataset pada model transfer learning CNN Mobilenet, Efficientnet, dan Nasnetmobile dengan dataset yang variasi jumlah dataset yang memiliki jumlah yang terbatas. Pada hasil dari penelitian ini, dari ketiga dataset yang digunakan sebagai dalam melakukan training pada model efisien transfer learning baik MobileNet, EfficientNet, dan NasNetmobile, teknik augmentasi zoom range ataupun random erase dapat meningkatkan akurasi pada dataset dengan jumlah 56 citra dan 222 citra, sedangkan pada dataset dengan jumlah 500 data citra, semua teknik augmentasi terbukti dapat meningkatkan akurasi pada model arsitektur MobileNetV2 dan NasNetMobile. Sedangkan teknik fine tuning terbukti efektif dalam meningkatkan akurasi pada semua skala data yang kecil. AbstractToday deep learning technology is often associated with reliable processes (methods) when we have large amounts of data. In deep learning CNN (Convolutional Neural Network) plays a very important role which is often used to analyze (classify or recognize) visual images. In the era of deep learning models such as the complex Convolutional Neural Network (CNN) as it is today, it has new challenges such as gradient vanishing, overfitting due to dataset limitations, parameter optimization to hardware limitations. The MobileNet architecture was coined in 2017 by Howards, et al, which is one of the convolutional neural networks (CNN) architectures that can be used to overcome the need for excessive computing resources. This study aims to measure the effect of fine tune and dataset augmentation techniques on CNN mobilenet, efficientnet, and nasnetmobile transfer learning models with very small datasets. The results of this study are that of the three datasets used as the basis for training in efficient transfer learning models (mobilenet, efficientnet, and nasnetmobile), random erase and zoom range augmentation techniques dominate the increase in model accuracy. The amount of increase in accuracy after random erase or zoom range augmentation that occurs is about 0.03% to 0.1%.
HYPERPARAMETER TUNING ON RANDOM FOREST FOR DIAGNOSE COVID-19

Diagnosis of Covid using the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test requires high costs and takes a long time. For this reason, another method is needed that can be used to diagnose Covid-19 quickly and accurately. Random Forest is one of the popular classification algorithms for making predictive models. Random forest involves many hyperparameters that control the structure of each tree, the forest, and its randomness. Random Forest is a method which very sensitive to hyperparameter values, as their prediction accuracy can increase significantly when optimized hyperparameters are predefined and then adjusted according to the procedure. The purpose of doing hyperparameter tuning on the random forest algorithm is to increase accuracy in the diagnosis of covid-19. Searching for optimal values of hyperparameters is done by the Grid Search method and Random Search. The result explains that the Random Forest can be used to diagnose Covid-19 with an accuracy of 94%, and with hyperparameter tuning, it can increase the accuracy of the random forest by 2%.
MOBA GAME REVIEW SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM

MOBA as one of the many popular subgenres today, Mobile Legend, Arena of Valor, League of Legend and Lokapala based on the large number of downloads can be mentioned as popular MOBA games, but the rating of these four applications is below 4.0 on the Google Play platform Store, this happens because some users may think that this mobile MOBA game has several advantages, but also some disadvantages that affect ratings. This study aims to determine the results of sentiment analysis on mobile MOBA games using Google Play Store reviews. Then it is processed using Python programming to create a model with the linear kernel Support Vector Machine (SVM) algorithm to classify the dataset. From the results of the classification model test using 19,579 data, where there were 10,017 positive sentiment data and 9,562 negative sentiment data and the distribution of train data and test data was 70%: 30%, obtained an accuracy of 82.64% and then re-evaluated using the Cross Validation method using 5 times so that an accuracy of 83.38% is obtainedÂ Keywords: MOBA, Sentiment Analysis, Machine Learning, SVM, Cross Validation
THE IMPLEMENTATION OF NETWORK SERVER SECURITY SYSTEM USING HONEYPOT

Network Server security is an important aspect of ensuring the integrity and availability of information systems. This research aims to implement network Server security at Muhammadiyah University of East Kalimantan using Honeypot technology. Honeypots are used to attract the attention of attacks and monitor suspicious activities on the network. The research method used is NDLC (Network Development Life Cycle), which includes the design and implementation of Honeypots, as well as the collection and analysis of detected attack data. The research results show that by using three attack techniques, namely Slowloris attack with a Honeypot processing time of 2 seconds and Snort processing time of 180 seconds, GoldenEye attack with a Honeypot processing time of 2 seconds and Snort processing time of 180 seconds, and LOIC tools with a Snort processing time of 180 seconds. Honeypots cannot identify DDoS attacks because they focus more on attacks such as penetration attempts or other suspicious activities.
Penguatan Verbal dan Nonverbal dalam Pembelajaran Bahasa Indonesia Siswa Kelas XII SMA Negeri 11 Makassar

This study aims to describe the form of verbal and nonverbal reinforcement in Indonesian language learning for students of class XII SMA Negeri 11 Makassar. The approach in this research uses a descriptive qualitative approach, the data source is the XII grade Indonesian language teacher. Data collection techniques in this research are recording techniques and note-taking techniques. Data analysis techniques used data reduction, data presentation, and conclusion drawing. The results showed that the grade XII Indonesian language teacher had implemented verbal form of reinforcement in Indonesian language learning and nonverbal form in Indonesian language learning. The reinforcement given is quite varied, but the diction needs to be developed again.
Training on interactive learning media for elementary school teachers in Purworejo District

Preparing students to be ready to compete in the 4.0 era using the implementation of the available curriculum is one of the teacher's tasks in the classroom. One of the problems that occurred in several elementary schools in Purworejo district was that learning still used conventional learning in the form of lectures and questions and answers, and the evaluation media used did not vary. For this reason, the introduction of interactive electronic game media based on QuizWhizzer can be the right solution as an evaluation tool. The purpose of this program is to increase teachers' understanding, knowledge, abilities and skills in creating interactive learning media using QuizWhizzer. The method used in this program is service learning with 3 stages, namely preparation, implementation and evaluation. This program was attended by 15 elementary school teachers who were held at the Sambeng Public Elementary School, Bayan sub-district, Purworejo Regency. The results obtained from this training are increased teacher understanding, abilities and skills in creating interactive and interesting evaluation tools.
Analysis of Autonomous Learning as Method in Learning English: A Case Study

In the modern era, teaching English is the main role for making students be able to get a skill and communicate with foreigners on social media and reality. English teaching needs teachers to teach students to develop both academic and personal abilities of English. English Teachers often used new methods and strategies in learning English and even these methods were often evaluated every week but, why there were still many students who had not been able to understand English in terms of theory and practice. Autonomous learning was a student-centered language learning method in order to be able to practice and that its approach made students autonomy focused on utilizing of cognitive and metacognitive resources to shape the English learning process. The writer uses library research (library research). Library research is research in which data collection techniques are carried out by reading various literature related to information and relevance to the research topic. As for the type of literature research that is being carried out, namely field research, research uses a descriptive type, namely to describe in detail about reality or phenomena by providing criticism or research on these phenomena. The approach used by the author in this study is a qualitative approach. This qualitative approach is used to solve actual problems by collecting, compiling, analyzing and classifying data. Various types of data sources in articles in the form of journals, research reports, scientific magazines, newspapers, relevant books, seminar results, unpublished scientific articles, sources, bibliographical letters, video graphics and so on
A CRITICAL DISCOURSE ON THE RELATIONSHIP BETWEEN SCIENCE, TECHNOLOGY, INNOVATION, AND INTELLECTUAL PROPERTY IN AFRICA

BOOK REVIEW: CB Ncube Science, Technology &amp; Innovation and Intellectual Property: Leveraging Openness for Sustainable Development in Africa (2021) 1st ed 1-213, Juta and Company Ltd, ISBN 978 1 48513 765 8.
Industry 4.0 Technologies and Environmental Performance: The Moderating Effect of Coercive Power

The current study aimed to identify the impact of the big data analytics and internet of things on environmental performance in the manufacturing firms operating in Jordan, as well as to identify the moderating effect of coercive power. Structural equation modelling is used through the Smart-PLS program on a sample of 329 participants. Empirical results showed that each of all hypotheses is accepted. In general, this study made a theoretical and managerial contribution to the industry 4.0 and sustainability literature.
Flow of Control Using if Statements

In this chapter we are going to look at the if statement in Python. This statement is used to control the flow of execution within a program based on some condition. These conditions represent some choice point that will be evaluated too True or False.
Sets

Python provides many different container or collection types. This chapter focuses on the set collection. This collection is used to hold unique immutable collections of objects. It is a mutable collection and so the values it holds can change over time but does not allow duplicate values. It also supports basic set like operations such as intersection, union and difference.
A multi-authorisation blockchain-based access control for cloud healthcare system

Cloud services became less efficient to health organisations since they deal with sensitive medical data. The digital format leakage of a patient's health information with or without knowledge of the cloud provider may cause serious problems that require special consideration. This raises the question of whether we should trust the provider in the first place or if any other third party may be involved. In this paper, we extend the CP-ABE scheme with a (t, n) secret splitting method alongside with cloud and blockchain technology. To ensure a fine-grained access control, we propose a multi-authorisation scheme where the data user can retrieve the access from the data owner or from a group of certified users when he cannot give access himself. The data user reconstructs the decryption key from only t of n delivered authorisations, and preserves that only target users can read data, which even the members of the multi-authorisation group cannot.
Students’ self-consciousness of comic strips as narrative writing teaching aids

The use of comic strips as teaching aids for narrative writing is the subject of this study. This study used comic strips in the context of narrative writing to examine students' feelings of self-consciousness. The researchers conducted a case study to gather qualitative information for this study. A questionnaire was used as a research instrument in this study. The form of the questionnaire used by the researchers is a close-ended question with a short rating questionnaire (strongly agree, agree, neutral, disagree or strongly agree) which consists of ten statements. To analyze the data, the researchers used the formula of the computation for calculating the data to be a percentage of each student's response to each questionnaire item by presenting them on the table and then described it as the questionnaire was examined. The findings indicate that using comic strips as teaching aids for the narrative writing curriculum is well received by students. It showed that the students gave positive responses towards the implementation of comic strips as teaching media in narrative learning material. Almost all of the students in the classroom were interested and enjoyed it while the researchers implemented the comic strips. The students are active in class activities. They felt using the comic strips as a learning media makes them better able to develop their imagination and create ideas for developing the stories discussed because, in the comic, the explanations accompanied by pictures and dialogues make it easier for them to interpret the stories or material discussed, even though in terms of language skills and vocabulary are inadequate.
New Perspective on Progressive GANs Distillation for One-class Anomaly Detection

One-class anomaly detection is conducted to identify anomalous instances with different distributions from the expected normal instances. For this task, an Encoder-Decoder-Encoder typed Generative Adversarial Network (EDE-GAN) in previous research has shown state-of-the-art performance. However, there is a lack of research exploration on why this structure has such superior performance and the impact of hyperparameter settings on model performance. Therefore, in this paper, we first construct two GAN architectures to study these issues. We conclude that the following three factors play a key important role: (1)&#xA0;The EDE-GAN calculates the distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruction error between images. (2)&#xA0;Unlike other GAN architectures, the EDE-GAN model always obtains best results when the batch size is set to 1. (3)&#xA0;There is also evidence of how beneficial constraint on the latent space are when engaging in model training. Furthermore, to learn a compact and fast model, we also propose a Progressive Knowledge Distillation with GANs (P-KDGAN), which connects two standard GANs through the designed distillation loss. Two-step progressive learning continuously augments the performance of student GANs with improved results over single-step approach. Our experimental results on CIFAR-10, MNIST, and FMNIST datasets illustrate that P-KDGAN improves the performance of the student GAN by 2.44%, 1.77%, and 1.73% when compressing the computation at ratios of 24.45:1, 311.11:1, and 700:1, respectively.
The Effect of Using Think Pair Share to Improve Students’ Reading Skills

Reading is an active process of getting information and understanding from printed text media. Reading is one of the most important skills in language learning. Reading has challenges for many students; most students find it difficult to understand the topic of the text. Then, students are also passive in class. So, they couldn’t answer the question properly. Therefore, the teachers are expected to use a learning model that can overcome the problems in reading. This research aims to find out the effectiveness of Think Pair Share to improve students’ reading skills. This study was conducted at tenth grade students of MA Shofa Marwah which consist of 36 students. This research was quantitative research using quasi-experimental design with pre-test and post-test design. The data were collected through test in the form of multiple choice test. The mean score of the students in the experimental group was 88 while the mean score of the students in the control group was 72.19. The T-Test analysis showed that Sig. (2-tailed) was 0.000 which was less than 0.05 and the T-Test was more than T-table (8.514 &gt; 2.04). The result indicated that the use of Think Pair Share was effective toward students’ reading achievement.
The Empirical Investigation of the Relationship between Banks’ Lending Rate and Coffee Export Growth in Tanzania

This paper examines the relationship between banks’ lending rate and coffee export growth in Tanzania by the help of Johansen Co-integration test and vector error correction (VECM) model. The research results suggested that there is a negative relationship between banks' lending rates and coffee export growth both in the short run and long run. In the long run, the study found that, a unit change in BLR results in a 0.1936 percentage point decrease in the coffee export growth; while, in the short run, the study suggests that, a unit change in banks’ lending rate results in a 0.0303 percentage decrease of the coffee export growth, ceteris paribus.&#x0D; This result supports the argument that, as the cost of loans became cheaper (the low-interest rate charged on loans by commercial banks) will attract farmers to borrow for purchasing farm inputs and expanding agricultural production and hence this will intern enhance export growth, and the high-interest rate correlated with inhibited growth in the coffee exports growth.&#x0D; Based on the results obtained, this study recommends that, the monetary policy of the country periodically has to influences outcomes in the agriculture sector especially exportable crops such as coffee through easier monetary conditions. This will promise the availability of agricultural financing through borrowing from lending institutions at an affordable borrowing rate. Along with this, the availability of farm inputs especially fertilizers, mode seedlings, and pesticides has to be assured to farmers; which will undoubtedly produce the desired impact and lead to optimum productivity and crop quality to enhance exports.
Integration of Blockchain and AI: Exploring Application in the Digital Business

Artificial intelligence (AI) and blockchain are two disruptive technologies that have emerged from the Fourth Industrial Revolution (IR4.0) and have brought about significant transformations in the industry. The convergence of artificial intelligence (AI) with blockchain technology presents significant opportunities for the development of novel business models facilitated by digitalization. The current body of research pertaining to the integration of artificial intelligence (AI) with blockchain technology is available, although there is a lack of comprehensive understanding of the practicality and effectiveness of this integration within the business context. In order to bridge this existing knowledge gap, the primary objective of this research endeavor is to comprehensively analyze and delineate the various applications and advantages of integrated artificial intelligence (AI) and blockchain platforms within diverse sectors of the business industry.
Students’ Perception of Using Podcasts to Improve Listening Skills at the 3rd Semester Students of English Department at Universitas Bandar Lampung

The process of developing listening skills involves capturing the speaker's words, grasping their intended message, and encompassing the roles of both speaker and listener, along with the auditory aspect. This study aims to investigate how students perceive the use of podcasts for enhancing listening skills among 3rd semester students in the English Department of Universitas Bandar Lampung. Employing qualitative methods, the research was conducted with a participant group of 34 students from the university's English Department. Data collection encompassed surveys and interviews involving 5 students. The collected data was analyzed by merging insights from both the surveys and interviews. The findings demonstrate noticeable enhancements in students' listening skills through the integration of podcasts as a learning medium. Consequently, students' viewpoints regarding podcast utilization for improving listening skills exhibited advancements in effectiveness and distinctiveness. Students expressed appreciation for the captivating and informative nature of this learning medium.