Reviewerly/exHarmony huggingface dataset:
Revolutionizing College Elections with a Secure Blockchain Voting Solution

Conventional voting procedures are being used for the current round of elections. They do not guarantee the voters' complete confidentiality and safety in voting. Additionally, the system does not have complete safety because it is possible for anyone to interfere with the counting of the votes and change the outcome of the election. Furthermore, this should be done in an open and honest manner. Online voting is getting increasingly common in today's society. It has the ability to save administrative costs while raising voter turnout. With Block chain technology, the current disruptive technology, voting methods may be made more robust. In this article, we have proposed a solution that uses Block chain technology and addresses all of the problems outlined in the previous sections about college elections. Blockchain technology will not only protect user's privacy and the integrity of their data, but it will also offer greater openness to the electoral process. Students often hold elections on college campuses for a variety of reasons. Blockchain technology can benefit us in that context in several ways, among them increasing transparency. With the help of Blockchain technology, we streamlined an electronic voting system to offer security and authentication while also allowing individuals to vote without having to wait in huge queues
Factors Influencing Users' Perspective on Adopting Cloud Computing Framework in Higher Educational Institutions of Bangladesh

To date, various studies on cloud computing adoption have highlighted the advantages of using cloud computing in HEIs around the world. However, there has been a dearth of research in finding out the factors that affect cloud computing adoption in the HEIs of a developing country, Bangladesh. This sequential mixed method study involves interviews and a questionnaire survey among 481 students and faculty members in Bangladeshi HEIs. It has used the Statistical Package for Social Sciences (SPSS v.22) and SmartPLS (v3.2.7) software for the analysis of the questionnaire responses. The researchers have designed the conceptual model of the study based on UTAUT model. The findings reveal that perceived benefits <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$({t-value} =5.681)$</tex> and social influences <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(t- {value} =2.094$</tex> ) have significant positive influence on the intention to use of cloud computing. In contrast, technophobia <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(t-value =2.123$</tex> ) and the quality of Internet infrastructure <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(t-value =3.752)$</tex> have significant negative effects on the intention behind the actual use of cloud computing. On the other hand, perceived risk, the lack of knowledge and awareness of cloud computing, facilitating conditions, complexities and skills transferability have non-significant influence on the intention of actual use of cloud computing. The results of this study will help policymakers of Bangladeshi HEIs to ensure the effective adoption of services provided by cloud computing application by the faculty members and students. Moreover, the study sets an example for other developing countries in the world by highlighting the factors that affect cloud computing adoption among faculty members and students in HEIs.
Automatic Annotation of Metadata in Power System Databases Based on Correlation Feature Selection and Natural Language Processing

The task of annotating metadata in power system databases has become more challenging due to the exponential growth of data in these databases. In order to tackle this challenge, we propose a new approach for automating the annotation of metadata. We begin by using correlation feature selection to identify the most relevant features for annotation. We then apply natural language processing techniques to extract semantic information from these selected features. The results of our experiments show that our method achieves a high level of accuracy in metadata annotation and also reduces the time required for annotation. In conclusion, our approach provides a streamlined and efficient solution for automatically annotating metadata in power system databases.
Metaverse for Collaborative Online Learning: Short Review

In online learning, learning management systems are often used to enable students to learn together through asynchronous communication or synchronous communication. It is found that teachers have already started to use modern technology tools to simulate virtual environments. These tools enable learners to gain interest and understanding from the online lessons. Furthermore, collaborative learning among learners themselves and between learners and teachers is a huge success of online learning. Metaverse is an educational tool that enables learning environments in certain topics to ensure that students understand theories alongside practice. This way, learning results in the achievement of the objectives of the given set of subjects. This preliminary study of relevant literature is a compilation of Metaverse scholarly articles. It shows a high possibility that online collaborative learning through Moodle learning management system can certainly bring Metaverse to support both theoretical and practical online learning.
Use of SHAP for Interpreting a Drone ClassificationModel Based on Statistical Radar Features

The aim of this paper is to interpret a Light Gradient Boost-based model developed for generic radar classification problems. Light gradient boosting machine (LGBM) being a means of accelerating gradient-boosting decision trees, is apparently helpful in real-time radar-based applications. In this paper, LGBM is developed around statistical features which include the maximum, average, standard deviation, skew, kurtosis, and Shannon entropy of the real-valued vectors. Vectors are complex and real-valued data structures that are generated through the extraction of target range cells along the Doppler dimension at the target range. A conventional Explainable Artificial Intelligence (XAI) method has been used to analyze the trained LGBM model, thereby leading to some recommendations for arriving at features that required to be tuned. SHAP (Shapley Additive Explanations), a post-hoc model agnostic XAI method, is used to provide a global explanation by considering the entire set of data instances followed by an explanation with respect to the local impact of particular data instances on the overall score.
Classification of Soil Fertility Level Based on Texture with Convolutional Neural Network (CNN) Algorithm

This study aims to classify the level of agricultural soil fertility based on texture. This study proposes a new approach to categorizing soil fertility levels based on soil texture using the Convolutional Neural Network (CNN) algorithm. The data is divided into three parts, with a percentage of 80% training data for 1120 images and 10% for each test and validation data for 140 soil images with a data size of 224x224 pixels. Several trials have been conducted by tuning the learning rate, optimizer, batch size, and augmentation. The results show that using the Adam optimizer, learning rate 0.001, batch size 8, and augmenting the dataset produces the best accuracy of 94.24% at best epoch 73. This research shows that by tuning the CNN parameter, it can classify the level of agricultural soil fertility based on its texture.
Security System To Prevent Terrorism Actions Using Face Recognition In Worship Building

Worship activities are essential things that need to be done by every religious community in Indonesia. This activity is carried out in places of worship that need to be respected and guarded to create a sense of security for people who worship and make worship activities run smoothly. However, the problem of acts of terrorism that have recently occurred in places of worship threatens the sense of security that exists in people when they carry out worship activities. From the problems found, researchers designed a security system in places of worship using face recognition technology. The method used in this research is the prototype model. Data is collected by conducting interviews and literature studies. Researchers will use deep learning algorithms such as MTCNN and ResNet to develop a security system that uses face recognition. The result of the research is a security system in places of worship that can monitor near the entrance of places of worship to reduce the possibility of acts of terrorism in places of worship.
Integrated Attendance System using NFC Technology

This research aims to design and develop prototype for an integrated attendance system for University classes in this case the Universitas Klabat (Unklab) is chosen are the object of our research. The technology that we adopt is the Near Field Communication (NFC) wireless technology which enables the transfer of data and information between adjacent devices. The main reason we choose this technology is because most of the smart devices such smartphone and etcetera are being supported by NFC, hence any plugging device for RFID reader are no longer being needed since the NFC technology is being provided. This designed system is expected to aid and simply the complexity of the process for checking the student attendance in every classes so that the process will be more accurately, efficiently, and can reduce the risk of doing cheating or any kind of unwanted actions performed by the students against their classes absences. This research was conducted using the Prototype Process Model as the research method. The programming technology that we use are the Java Script, HTML, and CSS, as the programming language. To support the data collection we adopt the Firebase technology as for cloud computing database. The result of our research is the integrated attendance system using NFC that can be used by lecturers in a University.
Accuracy of Comparison Random Forest, Gradient Boosting Tree, Decision Tree, and Naïve Bayes Algorithms in Predicting the Size of Companies Where Data Scientist Works

Therefore, this research discusses the comparison of accuracy between machine learning algorithms such as Naïve Bayes, Decision Tree, Random Forest, and Gradient Boosting Tree for data scientist salary data in predicting the size of the company where they work. This research method involves data from the Kaggle platform. The data includes attributes such as salary paid in the year, salary currency, employee type, company location, job title, salary in USD, experience level in the year, salary, employee residence, company size, and remote ratio. The results of this study show that the three algorithms which is Random Forest, Decision Tree, and Gradient Boosting Tree, have a high level of accuracy compared to Naïve Bayes in predicting the size of the company where data scientists work. However, the performance of Random Forest is less optimal due to unbalanced data which results in the results of Random Forest being less accurate than Decision Tree. A high level of accuracy can affect the classification results so that the better the accuracy level, the better the classification results. Meanwhile, Gradient Boosting Tree obtained excellent accuracy results from the three machine learning algorithms. The conclusion of this research is that Gradient Boosting Tree and Decision Tree can be a good choice to predict the size of the company where data scientists work. However, it is important to ensure that the data is balanced so that the Random Forest algorithm can run more optimally.
Understanding Support Method for Requirements Specification Using Description Status Based on Page Trend

Automatic text summarization based on natural language processing is used to efficiently understand large-scale documents. The description status, such as where in the requirements specification the important concepts are described, is considered to be effective in comprehending the requirements specifications. We propose a method for supporting document comprehension by utilizing the description status represented based on page trends. According to the evaluation, the description status summarization information from page trends was shown to be effective for novice engineers who sufficiently understood the purpose of reading comprehension of requirements specifications; this serves as support information for developing analysis methods or setting up plans while reading the text.
Sucurity Protection Framework for Power 5G Services Based on Zero-Trust Model

The Energy Internet is one of the core components of the Industrial Internet, with trillions of terminals connected to the network, linking all aspects of energy production, transmission and consumption online, and generating massive amounts of data for comprehensive awareness. The traditional security architecture is based on “defense in depth + boundary protection”. However, with the development of the Energy Internet, the security boundary is gradually broken and completely blurred, and the boundary-based security protection system gradually fails to meet the security needs of the Energy Internet. This paper proposes a zero-trust based adaptable security protection framework for Power services, which uses differentiated slicing to achieve security isolation for Power services, low latency for Power services through UPF sinking and FlexE, flexible and adjustable zero-trust security management through exposure security capability. The framework can achieve the low latency and high security requirements of terminal access under the power 5G network, and provide communication reliability for the ‘last mile’ of the smart power loT.
The Applications and Presentations of Drones in Staged Performances and Contemporary Art

With advancements in UAV technology and the gradual reduction in their size, UAVs have been increasingly integrated into both outdoor and indoor stage performances since the early 2010s. Current UAV performances can be broadly categorized into smaller-scale indoor stage drone shows and large-scale swarm drone shows. This paper aims to discuss the applications and various expressions of drones in smaller-scale stage performances and contemporary art.Drones are not mere images on a screen; they are physical objects with the ability to move with intent and direction. They share the same space as human performers and can interact with them through light, movement, sound, or physical contact. Drones can also enter the audience space, breaking the fourth wall on the stage and engaging closely with the audience.
Modeling Land Classification and Corn Planting Phases Using KSA Observation Points with CNN and SMOTE Hybrid Methods

In 2020, BPS and BPPT developed an Area Sampling Survey (KSA) for maize to obtain accurate and precise food security data. On the other hand, the KSA Survey has several weaknesses in its application, one of which is the KSA Survey not being able to explore areas that can not be reached, and so on. To overcome this problem, we propose to use of remote sensing as a data collection technique. This study aims to obtain the best maize land classification model and corn planting phase using machine learning, deep learning, and the SMOTE method. The results showed that the best model for land classification and maize planting phase were CNN-1D-SMOTE 2 labels with 85% accuracy, 79% sensitivity, 63% precision, and f1-score 70% and SVM without SMOTE 58% accuracy, respectively. Sensitivity 59.5%, precision 54%, and f1-score 55.7%. The estimated corn planting area for 2021 is 36,049.15 ha.
Aardvark: Comparative Visualization of Data Analysis Scripts

Debugging programs is one of the most challenging and time consuming parts of programming. Data science scripts present additional challenges as debugging often centers around more exploratory tasks, such as understanding the differences between results under different parameter settings. In fact, a common exploratory debugging practice is to run, modify, and re-run a script to observe the effects of the modification. Analysts perform this process frequently as they explore different settings and algorithms in their analysis. However, traditional debugging methods are not well suited to comparing across multiple executions of a script. They often require maintaining two instances of the debugging method and making manual, serial comparisons of program values. To address this gap, we present Aardvark, a comparative trace-based debugging method for identifying and visualizing the differences between two executions of data analysis scripts. Aardvark traces two consecutive instances of an analysis script, identifies the differences between them, and presents them through comparative visualizations. We present a prototype implementation in Python as well as an extension to support scripts in Jupyter notebooks. Finally, to demonstrate Aardvark, we provide two usage scenarios on real world analysis scripts.
Acquiring Testable NFRs Utilizing Goal Models Enhancing Application Requirements Analysis in Goal-Driven Software Product Lines

Software product line engineering involves reusing development assets to create a family of software systems with common features and few specific differences. Testing is crucial for evaluating software quality, encompassing both functional and non-functional requirements. However, non-functional requirements (NFRs) are often neglected in software product lines, with a primary focus on functional requirements during system configuration. This paper addresses the significance of early testing and the challenges of testing non-functional properties in software product lines. To ensure effective testing of non-functional aspects, clear and testable specifications for non-functional requirements are essential. Current practices often leave non-functional requirements unaddressed until system testing, lacking proper traceability and management. Additionally, systematic approaches are lacking to support non-functional requirements from the early stages of development, hindering their integration into product line development. In this research, we propose an approach that utilizes goal models to enhance application requirements analysis in goal-driven software product lines. By incorporating goal models, our approach enables the acquisition of testable non-functional requirements during the early stages of development. Our approach aims to improve the effectiveness and efficiency of testing non-functional properties and align them with the specified goals.
Secure Medical Data Management Based on Homomorphic Encryption and Secret Sharing

Medical data are highly sensitive and require secure storage systems to protect patients' privacy. Centralized databases pose significant risks in the context of a breach, since all data are concentrated in a single location. Additionally, privacy concerns often prevent medical institutions from sharing data, hindering potential research breakthroughs. To address these challenges, we propose a novel cloud storage system that utilizes homomorphic encryption and secret sharing. Our approach enables secure cloud storage of medical data and allows for secure data sharing without compromising patients' privacy. In addition to enhancing confidentiality, our proposed approach also improves data integrity and availability. We implement a proof-of-concept that demonstrates the feasibility of our storage system.
Comparison of Artificial Intelligence Models in Cross-lingual Transfer Learning through Sentiment Analysis

Cross-lingual transfer learning has become an effective way to overcome the limitations of sentiment analysis across various languages. Artificial intelligence (AI) models, particularly those built on transfer learning architectures, have demonstrated remarkable potential in this area in recent years. This paper provides an extensive evaluation of the role played by models in facilitating cross-lingual sentiment analysis through transfer learning techniques. The study provides valuable insights into selecting effective feature representations and extraction techniques for sentiment analysis tasks, enabling researchers to make efficient decisions and improve accuracy and performance in sentiment analysis applications. There are three models, neural network-based (NNB), K-Nearest Neighbor (KNN), and Graph Convolution Network (GCN) models that have been selected and applied over English data to Arabic data after training over other languages. The average accuracy of the Neural Network Based (NNB), K-Nearest Neighbor (KNN), and Graph Convolution Network (GCN) models is 56%, 50%, and 51%, respectively. The accuracy of the NNB model proved the highest, coming in at 56%, while that of the KNN and GCN models was slightly lower, at 50% and 51%, respectively.
A Transparent Blockchain-Based College Admissions Platform

The college admissions process is a very important step in every student's educational journey. Students submit a comprehensive application which is judged holistically by an admissions committee. Given the incredibly important nature of this process, it is crucial to ensure that it is fair to all and free from tampering. However, there has been a lot of unfortunate controversy surrounding admissions. Since admission to a top college is potentially life-changing, individuals often have incentives to behave dishonestly. Further, there have been accusations on the institutions themselves for giving preferences to the wealthy and well-connected applicants. This suggests the need for more transparency and fairness in college admissions. In light of this, we propose that many such concerns can be alleviated by using blockchains. Blockchains are an elegant solution since they naturally guarantee certain properties such as transparency and immunity from tampering. Thus, this paper describes the high level design of a system for an end-to-end college admissions platform which is based on the Blockchain. In particular, we depend heavily on thoughtfully designed smart contracts to process information and will be supported by decentralized data storage as well as a front-end. Our design is collaborative in that we integrate existing solutions for aspects such as certificate verification, to strengthen the transparency credentials of our system.
Trajectory Tracking and Obstacle Avoidance with Turtlebot 3 Burger and ROS 2

The paper presents the implementation of trajectory tracking and obstacle avoidance algorithms on a Turtlebot 3 Burger using the ROS 2 operating system. A proportional control with compensation is used for lemniscate and circle trajectory tracking. In addition, an potential artificial field (APF) algorithm is incorporated for obstacle avoidance in dynamic environments. In this approach, the robot is modeled as a particle immersed in a potential field that uses attractive functions to reach a destination point and repulsive functions to go around obstacles. Simulated tests in Gazebo software and experimental tests are performed to validate the performance of the algorithms. The results showed that the route followed meets the requirements of the problem, confirming that the proposed route planning approach is efficient.