Reviewerly/exHarmony huggingface dataset:
A New Integrated Approach for Cloud Service Composition and Sharing Using a Hybrid Algorithm

The concept of a “Smart City” emphasizes the need to employ information and communication technologies to strengthen the quality, connectivity, and efficiency of various municipal services. Cloud computing and the Internet of Things are shaping future tech. Both ideas greatly impact smart city application and solution development. Cloud computing is amazing at managing and storing remote service access. Several companies have switched to cloud leasing to reduce local resource burden. Due to the intricacy and flexibility of cloud-maintained services, selecting jobs that best suit client needs should be optimized. Quality of service criteria for each cloud service are the best tools for choosing and optimizing cloud carriers. Genetic algorithms (GAs) and ant colony optimization (ACO) are combined to make cloud computing. It is discovered that the recommended ACO + GA obtains an accuracy of 82% when compared to existing methods of energy- and reliability-aware multiobjective optimization method and the hybrid cuckoo particles swarm, artificial bee colony optimization (CPS + ABCO) where accuracy is 68% and 75%, respectively.
A Proposed Framework of Vulnerability Assessment and Penetration Testing (VAPT) in Cloud Computing Environments from Penetration Tester Perspective

Penetration testing is a process that focuses on finding security vulnerabilities in a target environment that could let an attacker penetrate the network or computer system or steal information. Due to the COVID-19 endemic, most employees still implement working from home or a hybrid approach, even though the number of new cases of COVID-19 is decreasing. However, working from home depends mainly on cloud computing applications that help employees efficiently accomplish their daily work. This situation also increased the number of data generated from various sources, so they may be exposed to different security risks. This research will propose a framework to conduct vulnerability assessment and penetration testing (VAPT) in cloud service models such as SaaS, PaaS, and IaaS from the perspective of penetration testers. This proposed framework is developed through the integration and mapping of existing frameworks and guidelines to conduct VAPT on testing components such as web applications, APIs, network testing, etc. In this proposed framework, the method of conducting VAPT for each cloud service model will be discussed in detail, from the planning and reconnaissance stage until the report is delivered to the cloud subscriber or cloud provider. An advantage of this proposed framework for the penetration tester is that there is still a lack of methods or guidelines for conducting VAPT that cover all the cloud service models in one comprehensive document
Blockchain Technology in Overcoming Security Threats for Smart Manufacturing System - A Systematic Literature Review

Blockchain is a new emerging technology that is mainly used to add security layers to many applications. Studies showed that businesses and manufacturing industries suffer from cyber-attacks, especially after the emergence of Industry 4.0 and IoT devices connected to the cloud for data sharing purposes, these interactions between IoT devices and the information involved in the transactions between companies and customers produce a massive amount of data daily. This phenomenon becomes an easy target for attackers due to the security limitations, data corruption and compromised reliability of the traditional manufacturing systems during data transferring. Many have focused on the methods to address these security limitations of the manufacturing businesses. This paper shed the light on the major smart manufacturing sectors that can potentially benefit from blockchain technologies, including cloud manufacturing, collaborative manufacturing, digital twin manufacturing, robonomics based smart manufacturing and the supply chain industry. The blockchain can enhance the security by creating a decentralized environment for the data to be stored, which will make the data stored immutable for improved security. These immutable data are nearly impossible to manipulate, replace, or falsify the data stored on the blockchain secured network.
Coverage of Artificial Intelligence in Media

Artificial intelligence (AI) is an important catalyst that enables media associations to optimize and ameliorate their operations. The aim of the paper is to emphasize the significance and eventuality of AI during high- performance data analytics and media content generation. The focus is on responsible and ethically respectable use of AI, which imposes the need to continuously make trust, through multitudinous challenges, during the operation of implicit pitfalls. Standardization and obligatory publication of AI product data can pave the way for increased translucency and trust structure. Else, the power of AI, which has a huge eventuality to contribute to a better quality of life, could be converted into a suicide device.
Bitcoin Price Prediction Using Hybrid LSTM-GRU Models

Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.
An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.
A Comparative Study of Implicit knowledge of Creative Teachers among Middle School Teachers in South Korea and China

Objectives This research was to investigated the implicit knowledge of creative teachers possessed by middle school teachers in Korea and China. the study aimed to analyzed the cross-country variances and contribute to comprehension of the educational systems and cultural characteristics of both nations.&#x0D; Methods A survey was administered to a total of 174 middle school teachers in Korea and 187 middle school teachers in China. Participants were requested to freely crticulate the characteristics of creative teachers. The col-lected responses were subsequently analyzed from both cognitive and affective perspectives.&#x0D; Results The results revealed that Korean teachers ranked fluency, communication skills, originality, expertise, flexibility, respect, openness, consideration, breaking stereotypes, and encouragement as the foremost charac-teristics of creative teachers, in that order. Conversely, Chinese teachers mentioned originality, communication skills, expertise, applicability, flexibility, progressiveness, passion for education, fluency, respect, and humor as the key traits. While certain cognitive characteristics of creative teachers displayed similarities across both coun-tries, disparities were observed in their affective characteristics. Additionally, both countries placed greater em-phasis on the cognitive aspect of identifying a creative teacher, rather than the affective aspect. Chinese teachers exhibited a higher emphasis on the cognitive aspect than their Korean counterparts.&#x0D; Conclusions This study has effectively demonstrated dissimilarties in implicit perceptions regarding creative teachers between Korean and Chinese educators. The findings of this research are anticipates to enhance the un-derstanding of the educational systems and cultural characteristics prevalent in both countries.
Development of ethnoscience-oriented multimedia learning salt manufacturing process from conductivity materials

The purpose of this study was to obtain an ethnoscience study of conductivity material and to determine student responses to media. This research uses a descriptive qualitative method with the ADDIE development model. The research location is in one of the salt ponds in Bangkalan Regency and SMP Negeri 2 Kamal. The sampling method was a simple random sampling of as many as 10 students of class VII. Data collection techniques using interviews, questionnaires, and documentation. The percentage of student responses on the display aspect is 97.33% and the material aspect is 94.25% which is included in the very positive criteria.
Credit Card Fraud Detection using KNN, Random Forest and Logistic Regression Algorithms : A Comparative Analysis

Because credit cards are utilized so frequently, fraud appears to be a significant concern in the credit card industry. It is challenging to quantify the effects of misrepresentation. Globally, credit card fraud has cost institutions and consumers billions of dollars. Despite the existence of numerous anti-fraud mechanisms, fraudsters continue to seek out novel methods and strategies to commit fraud. An additional challenge in the estimation of credit card fraud loss is that the magnitude of unreported or undetected forgeries cannot be determined, only losses associated with those frauds that have been detected can be measured. Implementing effective fraud detection algorithms through the utilization of machine-learning techniques is crucial in order to mitigate these losses and provide support to fraud investigators. This paper presents a machine learning-based method for the detection of credit card fraud. Three methodologies are implemented on the raw and pre-processed data. Python is used to implement the work. By comparing the accuracy-based performance evaluations of k-nearest neighbor and logistic regression with Random Forest, it is determined that the former exhibits superior performance.
Comparative Analysis of Machine Learning and Deep Learning Models for Bitcoin Price Prediction

This research endeavors to forecast Bitcoin prices by employing a suite of machine learning and deep learning models. Five distinct models were employed: Random Forest, Linear Regression, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), each evaluated based on their R-squared scores. Notably, the models showcased diverse performances, with the ensemble learning approach of Random Forest exhibiting near-perfect accuracy, closely followed by GRU and SVM. The deep learning architectures, LSTM and GRU, demonstrated remarkable predictive capabilities, showcasing their adeptness in capturing intricate temporal patterns within the cryptocurrency price data. This study sheds light on the comparative performance of these models, emphasizing their strengths and limitations in predicting Bitcoin prices.
Exploration of Problems and Key Points in Database Design in Software Development

Starting from the necessity and principles of database design, this article explores the optimization issues. Firstly, analyze the necessity of database design, elaborating on effective management, maintainability, resource utilization, and running speed; Then, a series of issues in database management were discussed, such as user management, data object design specifications, and overall design ideas; Finally, the optimization issues such as normalization rules, inter table redundancy handling, query optimization, indexing, and transactions were elaborated in detail. In the software development lifecycle, database design is indispensable. Its role is not only to ensure the safety and reliability of data, but also to ensure the overall stability and speed of the system. Strengthening the rationality and optimization of design is the key to improving software quality.
Online Gaming Addiction Among UiTM Puncak Alam Pharmacy Students Amid COVID-19 Pandemic

Stress among the university students had been triggered by the long-haul social distancing during the pandemic. Many had turned to playing video games more than usual as a social lifeline that could be potentially addictive. This study aims to evaluate the level of video gaming addiction during COVID-19 pandemic particularly among the UiTM Puncak Alam students. A cross-sectional study design with self-administered online survey was used to achieve the targeted objectives. The survey included several sections such as frequency of playing video games and 7-Item Game Addiction Scale (GAS). More than half of the cohort were females (60%) with average age of 23.46 ± 3.64 years. The findings showed that on average, these video gamers played approximately 2.33 ± 1.83 hours daily or 3.82 ± 2.04 days weekly. This study revealed that the prevalence of pathological and excessive gamers was low among the pharmacy students even during the pandemic restriction.
Youtube Video as A Media to Improve English Vocabulary Mastery of Children Aged 4 – 6 Years at Bina Jaya Kindergarten

This research is classroom action research (PTK). This research aims to improve English vocabulary mastery through video media at Bina Jaya Kindergarten to make it even better. The subjects of this research were 13 Bina Jaya Kindergarten children consisting of 8 boys and 5 girls. This research was conducted at Bina Jaya Kindergarten Jl.protokol, Sawit Seberang, Langkat, North Sumatra. The data collection technique is carried out directly and is adapted to the observation format. The instrument used is observation of children in the process of learning to introduce English vocabulary through the use of Youtube video media and documentation. Based on the research results, it shows that with action, there is an increase in the pre-cycle percentage of student's learning achievements reaching 30.76% with the MB criteria. The percentage of children's learning achievement increased in cycle 1 when the percentage reached 69.71%. This percentage increased by 38.95% compared to the percentage in the pre-cycle. An increase also occurred in cycle 2 where the percentage of children's achievements reached 79.81%, an increase of 10.1% compared to the percentage in cycle 1. From the research results, it can be concluded that Youtube video media can improve the ability to master English vocabulary by listening to English vocabulary. , pronounce English vocabulary, mention English vocabulary and write English vocabulary.
Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages.Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm.Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.
Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.
Indonesian News Text Summarization Using MBART Algorithm

Purpose: Technology advancements have led to the production of a large amount of textual data. There are numerous locations where one can find textual information sources, including blogs, news portals, and websites. Kompas, BBC, Liputan 6, CNN, and other news portals are a few websites that offer news in Indonesian. The purpose of this study was to explore the effectiveness of using mBART in text summarization for Bahasa Indonesia.Methods: This study uses mBART, a transformer architecture, to perform fine-tuning to generate news article summaries in Bahasa Indonesia. Evaluation was conducted using the ROUGE method to assess the quality of the summaries produced.Results: Evaluation using the ROUGE metric showed better results, with ROUGE-1 of 35.94, ROUGE-2 of 16.43, and ROUGE-L of 29.91. However, the performance of the model is still not optimal compared to existing models in text summarization for another language.Novelty: The novelty of this research lies in the use of mBART for text summarization, specifically adapted for Bahasa Indonesia. In addition, the findings also contribute to understanding the challenges and opportunities of improving text summarization techniques in the Indonesian context.
YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions

Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation, YOLOv8n achieved approximately 58%, YOLOv8s scored around 68.5%, and YOLOv8m achieved roughly 68.9%. However, after applying horizontal flip data augmentation, YOLOv8n's mAP increased to about 60.9%, YOLOv8s improved to about 62%, and YOLOv8m excelled with a mAP of about 71.2%. Using horizontal flip data augmentation improves the performance of all three YOLOv8 models. The YOLOv8m model achieves the highest mAP value of 71.2%, indicating its high effectiveness in detecting objects after applying horizontal flip augmentation. Novelty: This research introduces novelty by employing the latest version of YOLO, YOLOv8, and comparing its performance with YOLOv8n, YOLOv8s, and YOLOv8m. The use of data augmentation techniques, such as horizontal flip, to increase data variation is also novel in expanding the dataset and improving the model's ability to recognize objects.