Reviewerly/exHarmony huggingface dataset:
Pairwise Test Case Generation using (1+1) Evolutionary Algorithm for Software Product Line Testing

Software product line SPLs, or software product lines, are groups of similar software systems that share some commonalities but stand out from one another in terms of the features they offer. Over the past few decades, SPLs have been the focus of a great deal of study and implementation in both the academic and commercial sectors. Using SPLs has been shown to improve product customization and decrease time to market. Additional difficulties arise when testing SPLs because it is impractical to test all possible product permutations. The use of Combinatorial Testing in SPL testing has been the subject of extensive study in recent years. The purpose of this study is to gather and analyze data on combinatorial testing applications in SPL, apply Pairwise Testing using (1+1) evolutionary algorithms to SPL across four case studies, and assess the algorithms' efficacy using predetermined evaluation criteria. According to the findings, the performance of this technique is superior when the case study is larger, that is, when it has a higher number of features, than when the case study is smaller in scale.
Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

An action recognition model based on 3D skeleton data may experience a decrease in recognition accuracy when facing complex backgrounds, and it is easy to overlook the local connection between dynamic gradient information and dynamic actions, resulting in a decrease in the fault tolerance of the constructed model. To achieve accurate and fast capture of human skeletal movements, a directed graph convolutional network recognition model that integrates attention mechanism and convolutional neural network is proposed. By combining spacetime converter and central differential graph convolution, a corresponding central differential converter graph convolutional network model is constructed to obtain dynamic gradient information in actions and calculate local connections between dynamic actions. The research outcomes express that the cross-target benchmark recognition rate of the directed graph convolutional network recognition model is 92.3%, and the cross-view benchmark recognition rate is 97.3%. The accuracy of Top-1 is 37.6%, and the accuracy of Top-5 is 60.5%. The cross-target recognition rate of the central differential converter graph convolutional network model is 92.9%, and the cross-view benchmark recognition rate is 97.5%. Undercross-target and cross-view benchmarks, the average recognition accuracy for similar actions is 81.3% and 88.9%, respectively. The accuracy of the entire action recognition model in single-person multi-person action recognition experiments is 95.0%. The outcomes denote that the model constructed by the research institute has higher recognition rate and more stable performance compared to existing neural network recognition models, and has certain research value.
Testing the Usability of Serious Game for Low Vision Children

Serious games are prodigious tools for building language, science and math knowledge and skills. Despite a growing number of studies on using serious games for learning, children with visual impairment have obstacles when playing the games. Low vision children have a visual balance that can be assisted with assistive technology. A 2D serious game for learning Mathematics is developed using Unity for low vision children. In order to enhance the game’s accessibility for low vision children, accessibility elements have been implemented in the serious game prototype. Those elements are screen design (buttons, menus, and navigation), multimedia (text, graphics, audio, and animation), object motion, and language. Upon completion of the serious game, usability testing was done to identify the accessibility of the serious game to low vision children based on the usability level. The observation technique is used for analysing the serious game. The overall usability score is good based on aspects of effectiveness, efficiency and user satisfaction tested.
Multimodal Deep Learning Approach for Real-Time Sentiment Analysis in Video Streaming

Recognizing emotions from visual data, like images and videos, presents a daunting challenge due to the intricacy of visual information and the subjective nature of human emotions. Over the years, deep learning has showcased remarkable success in diverse computer vision tasks, including sentiment classification. This paper introduces a novel multi-view deep learning framework for emotion recognition from visual data. Leveraging Convolutional Neural Networks (CNNs) this framework extracts features from visual data to enhance sentiment classification accuracy. Additionally, we enhance the deep learning model through cutting-edge techniques like transfer learning to bolster its generalization capabilities. Furthermore, we develop an efficient deep learning classification algorithm, effectively categorizing visual sentiments based on the extracted features. To assess its performance, we compare our proposed model with state-of-the-art machine learning methods in terms of classification accuracy, training time, and processing speed. The experimental results unequivocally demonstrate the superiority of our framework, showcasing higher classification accuracy, faster training times, and improved processing speed compared to existing methods. This multi-view deep learning approach marks a significant stride in emotion recognition from visual data and holds the potential for various real-world applications, such as social media sentiment analysis and automated video content analysis.
Design of a Decentralized AI IoT System Based on Back Propagation Neural Network Model

In the Internet of Things (IoT) era, when user needs are continually evolving, the coupling of AI and IoT technologies is unavoidable. Fog devices are introduced into the IoT system and given the function of hidden layer neurons of Back Propagation neural network, and Docker containers are combined to realize the mapping of devices and neurons in order to improve the quality of service of IoT devices. This study proposes the design of a decentralized AI IoT system based on Back Propagation neural network model. The testing data revealed that, at various data transfer intervals, the average transmission rate between the fog device and the sensing device was 8.265Mbps, and that the device's transmission rate could satisfy user demand. When the data transmission interval was 20s, the network data transmission rate was greater than 8.5Mbps and did not vary much when the number of data transmissions rose. The research demonstrates that the decentralized AI IoT system's network performance, which is based on a back propagation neural network model, can match user usage requirements and has good stability.
Towards Secure Blockchain-enabled Cloud Computing: A Taxonomy of Security Issues and Recent Advances

Blockchain technology offers a promising solution for addressing performance and security challenges within distributed systems. This paper presents a comprehensive taxonomy of security issues in cloud computing and explores recent advances in utilizing blockchain to enhance security and efficiency in this domain. We employ a systematic literature review approach to analyze various blockchain-enabled solutions for cloud computing. Our findings reveal that blockchain's decentralized and immutable nature empowers cloud computing services to establish secure and private data interactions. By leveraging blockchain's consensus mechanism, we demonstrate the feasibility of creating a robust platform for authenticating transactions involving digital assets. Through cryptographic methods, blocks of transactions are securely linked, ensuring data integrity. This paper provides a roadmap for understanding security concerns in cloud computing and offers insights into the potential of blockchain technology. We conclude by outlining future research directions that can drive innovation in this exciting intersection of fields.
A Mechanism for Bitcoin Price Forecasting using Deep Learning

Researchers and investors have recently become interested in forecasting the cryptocurrency price forecasting but the most important currency can take that it’s the bitcoin exchange rate. Some researchers have aimed at leveraging the technical and financial characteristics of Bitcoin to create predictive models, while others have utilized conventional statistical methods to explain these factors. This article explores the LSTM model for forecasting the value of bitcoins using historical bitcoin price series. Predict future bitcoin prices by developing the most accurate LSTM forecasting model, building an advanced LSTM forecasting model (LSTM-BTC), and comparing past bitcoin prices. This is the second step, if looking at the end of the model, it has very high accuracy in predicting future prices. The performance of the proposed model is evaluated using five different datasets with monthly, weekly, daily, hourly, and minute-by-minute bitcoin price data with total records from January 1, 2021, to March 31, 2022. The results confirm the better forecasting accuracy of the proposed model using LSTM-BTC. The analysis includes square error MSE, RMSE, MAPE, and MAE of bitcoin price forecasting. Compared to the conventional LSTM model, the suggested LSTM-BTC model performs better. The contribution made by this research is to present a new framework for predicting the price of Bitcoin that solves the issue of choosing and evaluating input variables in LSTM without making firm data assumptions. The outcomes demonstrate its potential use in applications for industry forecasting, including different cryptocurrencies, health data, and economic time.
Machine Learning Model for Automated Assessment of Short Subjective Answers

Natural Language Processing (NLP) has recently gained significant attention; where, semantic similarity techniques are widely used in diverse applications, such as information retrieval, question-answering systems, and sentiment analysis. One promising area where NLP is being applied, is personalized learning, where assessment and adaptive tests are used to capture students' cognitive abilities. In this context, open-ended questions are commonly used in assessments due to their simplicity, but their effectiveness depends on the type of answer expected. To improve comprehension, it is essential to understand the underlying meaning of short text answers, which is challenging due to their length, lack of clarity, and structure. Researchers have proposed various approaches, including distributed semantics and vector space models, However, assessing short answers using these methods presents significant challenges, but machine learning methods, such as transformer models with multi-head attention, have emerged as advanced techniques for understanding and assessing the underlying meaning of answers. This paper proposes a transformer learning model that utilizes multi-head attention to identify and assess students' short answers to overcome these issues. Our approach improves the performance of assessing the assessments and outperforms current state-of-the-art techniques. We believe our model has the potential to revolutionize personalized learning and significantly contribute to improving student outcomes.
New Real Dataset Creation to Develop an Intelligent System for Predicting Chemotherapy Protocols

Breast cancer is the most common cancer diagnosed in women. In developing countries, controlling this scourge is often problematic due to late diagnosis and the lack of medical and human resources. Automation and optimization of treatment is then needed to improve patient outcome. The use of medical datasets could, according to medical staff and pharmacists, assist them in clinical decision-making and would allow for better use of resources especially when limited. In our paper, a new real dataset was produced by collecting medical and personal data from 601 patients with breast cancer at the University Hospital Center (UHC) Mohammed VI of Marrakech. Data of women diagnosed with breast cancer from January 2018 at UHC were assessed. Most patients were 24-85 year-old, with an average age of 48.84 years. Patient age, performance status (PS), cancer stage and subtype, treatment patterns and correlations among the different variables were analyzed. The created dataset will help to determine the most appropriate treatment regimen depending on the individual characteristics of patients to allow for better use of limited resources.
The Implementation of Neo Vernacular Architecture on the Design of Information and Recreation Center of Lake Toba Tourism at Ambarita, Samosir Regency.

Tourism is one of the sectors that Indonesia relies on increasing state revenues. One of the tourist destinations that is still being developed is Lake Toba. This region has the potential to grow and compete to become world-class tourism. But in the process of development, there are still some obstacles. One of the obstacles is information services and promotion about tourism in the Lake Toba area that is still not maximized. Researchers identified the need for a facility that can accommodate promotional activities and tourism information through the design of the Tourism Information Center in the Lake Toba area, precisely in Ambarita Village, Samosir. Analysis and concept of design using an interpretation approach to Neo Vernacular Architecture. The result of this study is a design from the Lake Toba Tourism Information Center.
Marketization Strategy in Research Article Introductions by Indonesian Authors in English Language Education Journals

Writers should attract readers’ attention to read their work from the very beginning of their article; this can be done by addressing a centrality claim (henceforth CC). However, very few studies examining research article introductions (henceforth RAI) examined further how authors attract readers to the importance of the research topic. This study aimed at investigating the types of CCs used in the RAIs published in local journals in English Language Education (henceforth ELE) with different Sinta values. Forty articles were chosen from four national accredited journals in LE for this study. The results showed that the most frequent appeals found in the RAIs are appeals to salience followed by appeals to problematicity and appeals to magnitude. This is reasonable because a piece of research should be on an important topic, because of a practical problem on a popular topic to attract readers to read the entire article. Also, although the difference is not significant the higher the Sinta value of a journal the higher the frequency of different appeals used by the authors in the introduction of the articles published in the journal. This suggests that to improve the attractiveness of an article, authors should use several different types of appeals in their RAIs.
ACCELERATION OF LEARNING MANAGEMENT SYSTEM APPLICATION DEVELOPMENT IN THE EDUCATION SECTOR USING THE LOW CODE CONCEPT ON MICROGEN

The background of this research is to speed up application development which tends to take a long time. To respond this, researchers apply the concept of "Low-Code" referring to a software development approach by speeding up the manufacturing process by reducing the amount of code that needs to be written manually to be automated using the microgen platform. This research was tested on a case study of the development of a Learning Management System, making it faster and more efficient. System development used in this study is System Development Life Cycle (SDLC) method, which is a software development process model that is carried out in stages according to the existing mechanism. The backend created for this Learning Management System, uses the Microgen generator backend platform to automate the creation of the GraphQL API, and the database which is then consumed by the frontend which is the interface for the Learning Management System. This research resulted in a better understanding of how using automation tools like Microgen able to speed up the learning management system web development process. The research model with development (R&amp;D) applied in this study uses a systematic model that requires each process to be fulfilled sequentially starting from the first stage to the final stage to produce a product and determine feasibility through product validation being developed. By adopting this automation approach, the previously time-consuming time required to generate the backend can be significantly reduced. Thus, the result is greater efficiency in Web Learning Management System development, savings in human resources, and better opportunities to respond to changing market or user needs.
ASPECT EXTRACTION OF E-COMMERCE AND MARKETPLACE APPLICATIONS USING WORD2VEC AND WORDNET PATH

Aspect extraction is an essential element in Aspect-Based Sentiment Analysis (ABSA). Errors in determining aspects of ABSA will result in errors in determining the sentiment of an opinion and the accuracy value of ABSA. This study aims to obtain elements of opinion sentences on using e-commerce applications and marketplaces in Indonesia. Corrections of the statement were sourced from social media Twitter with the keywords "e-commerce" and "marketplace" from August 2020 to January 2022, and a total of 54,244 comments were obtained. Determination of the words that are candidate aspects is selected using POS Tagging for classes of noun singular (NN), noun plural (NNS), proper noun singular (NNP), and proper noun plural (NNPS).
Pengaruh Value Clarification Technique terhadap Hasil Belajar PKn Siswa Kelas IV di SD Negeri Cengkareng Barat 03 Pagi

The purpose of this study is to determine whether students who use the Value Clarification Technique model and students who do not use the model vary in their learning results for civics. This study employed a non-equivalent control group design and a quasi-experimental design method. Class IV students at SD Negeri Cengkareng Barat 03 Pagi made up the population of this study. A sample of 56 students were divided into two groups, class IV A and class IV B, with class IV A serving as the control class and class B as the experimental class. Techniques for gathering data that use test questions as the primary data source and focus on learning outcomes. The findings of hypothesis testing using the t test in the control class and experimental class at the conclusion of learning (posttest) yielded tcount = 2.935 with a significance level of 5% (=0.05) and ttable = 2.005, respectively. H0 is rejected because 2.935 &gt; 2.005 (tcount &gt; ttable). According to the findings of the research, adopting the Value Clarification Technique model has an impact on students' learning outcomes in class IV civics courses.
A Survey on Deep Learning-Based 2D Human Pose Estimation Models

In this article, a comprehensive survey of deep learning-based (DL-based) human pose estimation (HPE) that can help researchers in the domain of computer vision is presented. HPE is among the fastest-growing research domains of computer vision and is used in solving several problems for human endeavours. After the detailed introduction, three different human body modes followed by the main stages of HPE and two pipelines of two-dimensional (2D) HPE are presented. The details of the four components of HPE are also presented. The keypoints output format of two popular 2D HPE datasets and the most cited DL-based HPE articles from the year of breakthrough are both shown in tabular form. This study intends to highlight the limitations of published reviews and surveys respecting presenting a systematic review of the current DL-based solution to the 2D HPE model. Furthermore, a detailed and meaningful survey that will guide new and existing researchers on DL-based 2D HPE models is achieved. Finally, some future research directions in the field of HPE, such as limited data on disabled persons and multi-training DL-based models, are revealed to encourage researchers and promote the growth of HPE research.
PCOS DISEASE CLASSIFICATION USING FEATURE SELECTION RFECV AND EDA WITH KNN ALGORITHM METHOD

Polycystic ovary syndrome is an endocrine disorder of the ovaries that causes hormonal disturbances in women of reproductive age, where androgen secretion in the ovaries of women with Polycystic Ovary Syndrome (PCOS) is excessive compared to normal women. This usually occur in women with obesity which is characterized by irregular menstrual cycles, chronic anovulation, hyperandrogenism, and even infertility. Efforts are used to treat this disease in the form of hormone therapy, laparoscopic ovarian drilling, and in-vitro fertilization. However, these three therapies are focused on symptomatic therapy and are less effective in treating PCOS-related infertility. Detecting PCOS disease early is very necessary so that prevention and treatment can be carried out immediately. Therefore, a classification is carried out to detect PCOS disease by being able to analyze data that has a high degree of accuracy. The method used for the classification of PCOS disease is using the K Nearest Neighbor (KNN), method which previously carried out the feature selection process, namely the Exploratory Data Analysis (EDA), method which is used for the data analysis process by means of an analysis approach to data to find out the most accurate method and using the Recursive Feature Elimination and Cross-Validation (RFECV) selection method which ranks the features based on their level of importance to the prediction process. Further, the data classification process uses the K-Nearest Neighbors (KNN) algorithm. The results of the Exploratory Data Analysis (EDA) feature selection process produce 10 data attributes that are used and are continued by the Recursive Feature Elimination and Cross-Validation (RFECV) process by producing the 7 most important attributes used and finally the K-Nearest Neighbors (KNN) method has a high level high accuracy by producing an accuracy value of 93%, precision 82%, recall 100%, and F1 score 90%.
CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient (GFCC) was the most compelling feature compared to Mel-Frequency Cepstral Coefficient (MFCC), Inverse Mel-Frequency Cepstral Coefficient (IMFCC), Linear-Frequency Cepstral Coefficient (LFCC), and Bark-Frequency Cepstral Coefficient (BFCC). Additionally, we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.