Reviewerly/exHarmony huggingface dataset:
A Study on the Improvement Targets of Data Envelopment Analysis Models

In this paper, we focus on the characteristics of improvement targets generated by two distinct types of Data Envelopment Analysis (DEA) models: the conventional additive (ADD) model and the least-distance DEA model. DEA is a mathematical programming technique employed to evaluate the relative efficiency of decision making units (DMUs) that have multiple inputs and outputs. One of the notable aspects of DEA is its ability to generate improvement targets for each inefficient DMU to achieve efficiency. Thus, the concept of the least-distance DEA model has been introduced to generate the closest improvement target that closely resembles the evaluated DMU and can be easily achieved. To evaluate the effectiveness of the different improvement targets, we compare the improvement targets generated by the ADD and the least-distance DEA models. This analysis is performed using a time-series dataset comprising 86 retail companies in Japan. The results of the numerical experiments indicate that the improvement targets generated by the least-distance DEA model exhibit superiority in achieving efficiency for the inefficient DMUs. These findings shed light on the potential advantages and effectiveness of the least-distance DEA model in improving the efficiency of DMUs.
Distance Maximization and Defences on Deep Hashing Based Image Retrieval

Though being remarkably efficient in computation and storage, recent research demonstrates deep hashing based image retrieval models are also vulnerable to adversarial attacks. A substantial amount of defence techniques against adversarial attacks have been developed recently, the overwhelming majority focus on adversarial training. However, adversarial defence focusing on the data side, i.e., cluster-contrasting, still remains a paucity. Albeit a pioneer work makes attempt to maximise the average distances of image clusters by anchoring predefined hash centers, it is still insufficient for a robust defence due to no optimal guarantee and over-sticking to the global average distance. Further, the laser-focus of either adversarial training or cluster contrasting of existing methods hinder them from further robustness boosting by judiciously incorporating them together. In this paper, we propose a novel distance maximization algorithm for defence on deep hashing based image retrieval systems. The model finds the optimal maximum average distance between cluster centers, and then uses a heuristic method to increase the minimum distance in the worst-case by solving a maximum Boolean satisfiability (max-SAT) problem. Our proposed distance maximization algorithm is a boosting algorithm that can be incorporated into adversarial training to enhance or boost the robustness of retrieval systems. Experiments conducted on two datasets demonstrate that our algorithm can generate clusters with a maximised average distance, while the minimum distance is also increased by up to 33% over the state-of-the-art method, and robustness is improved by up to 23 %.
Simulation of Cascading Faults of Power Grid Under Extreme Ice and Snow Weather Based on BPA

The threat of extreme weather to power grid is in urgent need of corresponding research means. Power grid simulation tools can effectively improve the performance of power grid through pre-disaster warning. The fault of extreme weather in the power grid has the characteristics of mass occurrence and succession, and the traditional power system simulation software can not be directly used for automatic simulation and analysis. This paper develops a simulation method of power grid successive fault under extreme snow and ice weather based on BPA. A comprehensive power grid simulation framework including meteorological modeling, automatic simulation of cascading failures and result analysis is established, which extends the field of power system simulation to extreme weather and enriches the connotation and function of current power grid simulation.
Data Feminism Influence on Data Visualization

Data Visualisation is currently seen as a powerful communication tool, as the human mind is more receptive to visual information than words or raw data. However, most existing visualisations are done from an androcentric perspective. This article proposes Data Visualisation from a feminist perspective. To this end, concepts such as Feminism, Data Visualisation and Data Feminism will be addressed to arrive at the concept of Data Visualisation in Feminism, resulting in a set of guidelines and recommendations to be incorporated when designing Data Visualisations. As part of the work carried out, an existing data visualisation was selected and analysed, highlighting the guidelines of Data Feminism in Data Visualisation. As a result, a recreation of this visualisation is proposed, incorporating the Data Feminism guidelines. It was found that the resulting visualisation is different from the original and is aligned with the concepts of Data Feminism.
Efektivitas Penggunaan Aplikasi Quizizz dalam Model Pembelajaran Langsung (Direct Instruction) terhadap Hasil Belajar Peserta Didik Kelas XI MIPA 1 SMAN 11 Takalar Tahun Pelajaran 2021/2022 (Materi Pokok Laju Reaksi)

This research is a descriptive study that aims to describe the effectiveness of the Quizizz application on the direct instruction model to improve student learning outcomes in class XI MIPA 1 SMA Negeri 11 Takalar on the subject matter of reaction rate. The Subject of this research is class XI MIPA 1 which consists of 20 students. The data obtained using the instrument of student learning outcomes. The results of the descriptive analysis obtained the average value before treatment was 23 and after treatment was 80,9. And the average value of N-Gain is 0,76 in the high category. Based on the results of the study, it can be concluded that the Quizizz application in the direct instruction model is in the high category for improving student learning outcomes in class XI MIPA 1 SMAN 11 Takalar on the subject of reaction rates. &#x0D; Keywords: Quizizz application, direct instruction, learning outcomes, reaction rate.
A Data-Driven Forced Oscillation Detection Using Random Forest

This study focuses on crucial challenges in forced oscillations (FOs) detection in power system. Since FOs require specialised mitigation schemes in contrast to electro-mechanical oscillations, it is crucial to detect the same prior to implementation of any control techniques. This paper presents a timeseries classification approach to detect FOs in power systems using Random Forest. The dataset is generated on the IEEE 4- generator 11-bus system, which is utilised to create a 3D array that is exploited to train and evaluate the machine learning model. The robustness of the technique is assessed via confusion matrix, F1 score, and receiver operating characteristic (ROC). The performance of proposed methodology is compared with other machine learning methods such as deep belief networks and neural network pattern recognition.
Channel Truncation Inspired Short-Packet Covert Communication with Poisson Packet Generation

In this paper, we consider short-packet covert communications with Poisson packet generation. To guarantee an optimal prior transmission probability while improving the communication reliability, we propose a novel covert communication scheme based on the channel truncation transmission. For the proposed scheme, we derive close-form expressions for the lower bound on covertness constraint, the average packet error probability, and the effective covert throughput (ECT). Then, the transmit power and the channel truncation threshold are jointly optimized to maximize ECT, while the close-form expression for the optimal channel truncation threshold with a given transmit power is obtained. Our examination shows the superiority of the proposed transmission scheme relative to a benchmark scheme without channel truncation in terms of achieving a higher ECT.
Contract-Matching Incentive Mechanism for UAV-Assisted Mobile Edge Computing

In the unmanned aerial vehicle (UAV) assisted edge computing networks, the UAVs act as mobile edge nodes to provide flexible communication and computation services to user equipment (UE). To enable efficient resource trading between the UAV and UE, it is of paramount importance to design an incentive mechanism that compensates for the costs of the UAVs, while preventing the UAVs from falsely reporting their private resource information. To address this issue, we propose a contract-matching integrated incentive mechanism in a multi-UAV and multi-UE network, where the UEs are unaware of the UAVs’ locations and computation frequencies. To deal with the asymmetric information, we employ contract theory to maximize each UE’s expected utility while encouraging the UAVs to truthfully participate in the edge computing service. Then, we adopt the Gale-Shapley algorithm to establish the one-to-one association between UAVs and UEs. Finally, simulation results validate that our proposed mechanism can achieve individual rationality, incentive compatibility, and improve UEs’ average utility compared with the benchmark algorithm.
To Study The Impact Of Virtual Assistant Using Artificial Intelligence In Society

As technology continues to advance, the integration of Artificial Intelligence (AI) in various domains has become increasingly prevalent. This research paper explores the transformative impact of AI in the realm of virtual assistance for research purposes. The paper delves into the development and implementation of intelligent virtual assistants, leveraging sophisticated AI algorithms and natural language processing techniques. The primary objective of this research is to investigate how AI-driven virtual assistants can enhance and streamline research workflows across diverse disciplines. The study assesses the capabilities of these virtual assistants in information retrieval, data analysis, literature review, and collaboration facilitation. Special attention is given to the adaptability of these systems to different research contexts and the customization options available for researchers. The paper also examines the challenges and ethical considerations associated with deploying AI in research assistance, addressing issues related to data privacy, bias, and the responsible use of AI technologies. Additionally, the research discusses the potential benefits of AI-driven virtual assistants, such as increased productivity, improved decision-making, and the democratization of access to research resources. Several case studies and examples of existing AI-powered virtual assistants in the research domain are analyzed to provide insights into their effectiveness and user acceptance.
Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method

The main objective of this research is to uncover the important role played by the social media platform Twitter in shaping public opinion regarding the 2023 Civil Servant Candidate (CPNS) selection process in Indonesia. Using advanced techniques such as social network analysis and Python language processing, as well as the application of the Naive Bayes algorithm, this research carefully examines the conversation patterns and topic trends prevalent on Twitter during the CPNS selection phase. The findings of this research unequivocally highlight the enormous influence of Twitter on public sentiment related to CPNS selection, as demonstrated by the classification model's impressive accuracy rate of approximately 95.19%. In addition, this research successfully identifies the influential roles played by key actors, prominent accounts, and narratives in shaping public perceptions. These groundbreaking insights foster a comprehensive understanding of the dynamic nature of public opinion in the context of CPNS selection, providing an invaluable basis for designing more effective communication strategies for the government and prospective civil servants.
Tahapan Pencarian Informasi Mahasiswa (Studi Kasus Program Magister Kenotariatan Universitas Sumatera Utara)

The purpose of this study was to determine the information search behavior of USU Master of Kenotariatan students. This research uses a descriptive method with a qualitative approach. The data of this research was taken by using interview techniques with informants as many as 4 people. The results showed that: First, at the starting stage the informant will first understand what information he needs and discuss with others about the research being carried out; Second, at the chaining stage the informant will see other references that might be used; Third, at the browsing stage the informant will search for information via the internet or by visiting the library; Fourth, at the monitoring stage the informant wants to stay up to date by visiting an information source regularly, or by asking directly to a lecturer or colleague; Fifth, at the differentiating stage the informant will distinguish the information found based on the relevance to the topic and the age of the information; Sixth, at the extracting stage the informant will take the important points of the information that has been found by reading and summarizing; Seventh, at the verifying stage the informant will double-check the identity and references used by the author; Eighth, at the ending stage the informant will re-evaluate the information to be used, At this stage, informants will also look for other information that might be useful for research. Based on the research results, all eight stages made by David Ellis have been carried out by informants. This research contributes that students who generally utilize libraries and the internet as a source of information and the campus provides information search education to students.
Darunnajah Vote System Application Design Using PHP Programming Language

Voting is the most important part of choosing leaders in an organization or institution, especially for those of us who use a democratic system. The election of organizational leaders in Darunnajah is still carried out conventionally, elections still use paper to select organizational leaders which is carried out by recording and counting the name or picture of one of the candidates. Creating the Darunnajah Vote System in the form of a web application for the election of organizational leaders in the Darunnajah environment can speed up the voting process. The web application is built using PHP and MySQL with features of user management, class management, candidate management, and E-Voting. This study aims to build an e-voting system that is expected to provide voting results quickly, and accurately and can be monitored in real-time during the implementation of the leader election. This system development method uses a web application-based SDLC Waterfall model. Testing using the black box method, every feature contained in the Darunnajah Vote System application, namely the Admin Login feature, User Login, User Voting, and User Data Processing features runs normally and functions properly.
Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking

This research aims to improve the software quality and effectiveness of zakat management by the National Amil Zakat Agency (BAZNAS) through the development of a software defect prediction model (SDPM). We used machine learning techniques and ensemble stacking approach on the "Masjid Tower" dataset containing 228 records and 34 attributes. The preprocessing process involved label encoding, feature selection with Pearson correlation, standard normalization, and the use of SMOTE to handle data imbalance. We performed hyperparameter tuning with grid search CV on Machine Learning algorithms such as Ada Boost and Gradient Boosting. The results showed that the ensemble stacking approach with a combination of Gradient Boosting, Ada Boost, Decision Tree, Bayesian Ridge, and LightGBM meta learner algorithms provided high accuracy with R2 score reaching 0.97, MAE of 0.037, and MSE of 0.006. This finding proves that the ensemble stacking approach is able to overcome the problem of software defects with accurate prediction results, provide useful guidance in the management of zakat and other software applications, and has the potential to improve software quality and the effectiveness of BAZNAS in managing zakat.
INTEGRASI TEKNOLOGI AUGMENTED REALITY BERBASIS MODEL SAMR (SUBSTITUTION, AUGMENTATION, MODIFICATION, REDEFINITION) DALAM PEMBELAJARAN IPA SEKOLAH DASAR

The importance of technology-based learning in the Society 5.0 era can increase students' digital literacy. One thing that can be done is to utilize Augmented Reality (AR) which can make learning more interesting, especially in representing science material at the elementary school level. This research examines the science learning process integrated with AR technology using the Substitution, Augmentation, Modification and Redefinition (SAMR) model, which is a stage in reviewing the implementation of a technology-based learning process focused on the use of AR in elementary schools. This research uses a case study research approach to explore in depth the data collection related to AR integration in science learning, focusing on a combination of qualitative and quantitative. The research results show that the utilization of AR technology using the SAMR model at elementary school level in the Cirebon Region is 25,28% in the "sufficient" category. The results of observations and interviews with teachers using the Puentedura Theory approach are that the substitution, augmentation and modification processes have been implemented as outputs of performance needs, but at the redefinition stage there are still difficulties due to the lack of infrastructure and a certain level of competency for teachers so that improvements are still needed skills for teachers in presenting an interesting learning process using AR technology.
EFEKTIVITAS PLATFORM PEMBELAJARAN MATEMATIKA BERBASIS DIGITAL UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH SISWA SEKOLAH DASAR

The lack of use of digital platforms as a learning tool at the elementary school level is one of the causes of low student learning outcomes in solving mathematical problems. This study uses a quantitative approach. The aim of the research is to improve elementary school students' problem-solving abilities with a digital-based platform in the form of live worksheets. The research population consisted of 300 students enrolled in a school. The data collection technique was a random sample (random sampling) of 49 students. The test instruments used are in the form of pretest and posttest sheets. The results showed that the pretest average was 21.24, the lowest score obtained on the pretest was 10, while the highest score was 25. Meanwhile, the average posttest score was also 21.24, the lowest score on the posttest was also 10, while the highest score was 25. It can be concluded that the average pretest and posttest scores are the same, namely 21.24. In addition, the range of scores from the lowest score to the highest score is from 10 to 25, indicating that there is variation in student performance but not too extreme. This concludes that the use of a digital platform in the form of live worksheets can improve students' problem solving abilities in mathematics learning.
PENGEMBANGAN BAHAN AJAR IPA BERBASIS E-BOOK SEBAGAI MEDIA PEMBELAJARAN KELAS V SEKOLAH DASAR

Based on the problems found in the learning process, it shows the limited variety of digital learning media that teachers have in delivering science material. The media used is limited to print media only. With simple media the teacher must explain the material repeatedly to students so that learning is not optimal and efficient. The research deSign used is research and development, a research method used to produce a product to be tested for feasibility and effectiveness. This research was conducted to develop products in the form of e-book-based science teaching materials as learning media for fifth grade elementary school students. The research deSign of the ADDIE development model goes through five stages, namely analysis, deSign, development, implementation and evaluation. Based on the results of the independent sample test, a positive hypothesis was obtained because there was a difference in the average student learning outcomes after using learning media in the form of e-book-based teaching materials on Heat and Transfer when compared to students who did not use media. There is a change in the average difference in student learning outcomes after using media in the form of e-book-based teaching materials. Based on the results of the research and discussion of media development, it can be interpreted that the developed e-book-based teaching material media is suitable for use in science learning on heat and transfer material in grade V SD, with appropriate criteria from media and material experts as well as teacher and student responses.
PENGEMBANGAN LKPD BERBASIS DISCOVERY LEARNING BERBANTUAN CANVA PADA KELAS V SEKOLAH DASAR

The problem found are that worksheet only focuses on answering question so that students' enthusiasm and activeness in learning process is low and students’ science score is low. This research aims to develop worksheet based on discovery learning assisted by Canva on heat and transfer material in 5h elementary school which will be tested for feasibility and effectiveness. This research and development uses the ADDIE research design and a true experiment design with a posttest only control design. The feasibility results by media experts are 100% (very feasible). While the feasibility by material experts is 77.3% (feasible). The effectiveness results using the independent sample t-test with a significance of 5% obtained 0.000, so that there is a significant average difference between the learning scores of the experimental class (which is 70.23) and the control class (which is 43.33). Worksheet based on discovery learning assisted by Canva on heat and displacement received a very good response from the teacher with 100% and a very good response from students with 98.6%. So conclution is the worksheet based on discovery learning was very feasible and very effective for use in 5th elementary school and received very good responses from students and teacher.
The Influence of Hyper Reading on Learning Media Development

This research focuses on problems regarding the reading habits of the digital native generation, known as hyper reading, which influences the development of learning media. Because currently, the development of learning media pays little attention to the reading characteristics of its users. So, this research aims to determine the influence of hyper reading on the development and packaging of learning media. This study used descriptive qualitative method. One way to obtain this data is through literature studies, surveys and focus group discussions. The survey results found that the characteristics of hyper reading that are most often felt by users are filtering, skimming, pecking, imposing, and filming. There are two categories of learning media that are influenced by hyper reading, namely print and non-print media. The characteristics of hyper reading can influence the development of learning media that involves the reading process. The most significant influence is in the message design or packaging of learning media materials in accordance with the five characteristics of hyper reading, namely filtering, skimming, pecking, imposing and filming.