Reviewerly/exHarmony huggingface dataset:
Analisis Trend Berita Media Online di Masa Pandemi Covid-19 dengan Metode Monte Carlo

The News reports for online media always follows the trending issues that are actually predictable. In general, online media news apart from presenting various events that have occurred, also focuses on reporting issues which is of concern to the public. During the Covid-19 pandemic, almost all mass media, including online media, continued to report various news about the Covid-19 case and its impact on society. The problem is how to understand the news accumulation in a certain period of time to find out the trend in reporting on Covid-19. Because the news has resulted in a huge accumulation of data related to Covid-19 news which can be used to build data mining. The purpose of this study is to analyze online media coverage during the Covid-19 pandemic using the Monte Carlo method to find out news trends. The process uses a data mining approach by collecting a lot of data from existing reports and processing the data to find important information from the data set. The data used to analyze news trends were taken from the main news on the online media Suarapembaruan.com for seven (7) months and processed using knowledge discovery in database (KDD) techniques. The Monte Carlo method is applied to data mining to calculate probability values or possible values that will occur in the future. The results of this study indicate that the trend of news related to Covid-19 in the future will be dominated by News on the Handling of Covid-19 with a trend percentage rate of 23.12 percent. Meanwhile, the news about the Covid-19 Case and the Impact of Covid-19 was getting lower with a trend rate of 21.82 percent and 19.22 percent. Meanwhile, The Other News with related to Covid-19 has a higher trend percentage of 35.84 percent
Penerapan Metode MOOSRA Dalam Seleksi Pengantaran Pemesanan Kue Online Menggunakan Pembobotan ROC

Each cake shop certainly has its own website that can be used to order cakes and of course has its own advantages and disadvantages which can be used as an assessment of the quality of the application, such as the types of cakes provided vary at more affordable prices and even the existence of discounted prices makes consumers more interested in ordering cakes online. The large number of these applications makes service users confused about choosing a better application among the good ones, therefore a study was made that discussed techniques for solving these problems using a decision support system. In this study, there are two methods used in the SPK method, including the ROC method which is used as an arrangement of weights of importance on five features that are used as rules which include application usability, Rating Reviews, Promos (Discounts), Cake Menu and Handling Fees. The second method is the Multiobjective Optimization on the Basis of Simple Ratio Analysis (MOOSRA) method which will be used to select 8 online cake ordering applications by ranking each alternative based on predetermined criteria. The final result after applying the two SPK methods is that Akul cake delivery is declared the best online cake delivery application with a preference value of 38.012.
The Effectiveness of using Android Based Chemical Ludo Game Media Reduction and Oxidation Reaction Material on Students Learning Outcomes

This research follow up research development of Educational Design Research with Plomp model that has been done the validity and practicality test but hasn’t been tested effectiveness using the android based chemical ludo game media for reduction and oxidation reactions material on student learning outcomes for class X SMA/MA. This aims for reveal the level effectiveness using the android based chemical ludo game media on reduction and oxidation reaction material to learning outcomes of class X SMA/MA.The design used was pretest-posttest control group design with random sample group. The sample of research were class X MIPA 4 with 34 students and X MIPA 3 with 34 students at SMAN Binaan Khusus. The research instrument is instrument test and questionnaires. The data using analyzed with N-Gain test and hypothesis test (t-test). N-Gain the experimental class was 0.79 with high category and the control class was 0.47 with medium category. The hypothesis testing which was carried out with the t-test with tcount (7.84527) &gt; ttable (1.668) at the real level α = 0.05. So the android based chemical ludo game media material for reduction and oxidation reactions effective in increasing learning outcomes for students significantly in the cognitive domain class X SMA/MA.
Characteristics of Extreme Rainfall Events in North Sumatra

Extreme rain is an event that has an impact on various sectors of life. Therefore, this study aims to determine the spatial and temporal characteristics of extreme rain events in North Sumatra Province in the time period between 1991-2020. In accordance with the WMO recommended rain index, the frequency and intensity of rain were calculated using 50 rain stations spread across North Sumatra where the data had passed the quality control test for empty data. Spatial patterns were analyzed by mapping the climatological mean of the indices then for trend patterns were tested using the Mann-Kendal non-parametric test. The results showed that extreme rainfall events with low frequency and intensity occurred on the east coast while high frequency and intensity occurred in mountainous areas. Based on the temporal trend test, it shows that in general an insignificant trend dominates in this area. Significant and consistently increasing trends are only found at several points, namely in the districts of Deli Serdang (5 station), Batu Bara (1 station), Humbang Hasundutan (1 station), Langkat (2 station), Labuhan Batu Utara (1 station), Medan (1 station), Pematang Siantar (1 station) and Serdang Bedagai (1 station). Meanwhile, a significant and consistently decreasing trend was found in Asahan Regency (1 station), Simalungun (2 station), North Tapanuli (1 station), and North Labuhan Batu (1 station).
A Conceptual Model For a Decentralized Central Bank Digital Currency

This paper develops a conceptual model for a decentralized central bank digital currency (CBDC) controlled by many central banks. A decentralized CBDC could potentially explore the advantages of digital technologies more deeply than centralized ones by making macro-fundamental reconciliations between economies. Furthermore, this concept could also potentially provide international liquidity for all (member) countries in the world sustainably, align macro-fundamental or eliminate global imbalances, and makes the international monetary system naturally more stable. A decentralized CBDC is a new kind of CBDC. This paper focuses on discussing the conceptual model and theoretical framework of how this system works in providing international liquidity and making the system balanced and stable.
Reseña. Tradición oral y música en Iniesta (Cuenca). Corpus poético-musical de literatura oral iniestiense. Antología sonora

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Overcoming Online Teaching and Learning Challenges in the Japanese Context

The COVID-19 pandemic presented many opportunities for instructors of foreign languages toexperiment with online learning technologies and gain knowledge that would assist them in theimplementation of these tools in the future. Even though COVID-19 pandemic generatedchallenges, these opportunities were plentiful. Therefore, this study investigates the challengesfaced by English as a Foreign Language (EFL) instructors while delivering online EFL classesduring the pandemic in Japan. The study investigates how they have overcome obstacles andwhat they have discovered from their experiences that can help online language instructorsimprove the quality of instruction they provide. Semi-structured interviews were conductedwith seven EFL instructors at the English Department of a Japanese university using MicrosoftTeams. During the interviews, participants revealed how they overcame challenges to intereststudents in their online classes and how they came up with unique techniques to teaching.Based on the findings, it appears that the online pedagogical training that instructors receivedwas inadequate. As a result, the study recommends that universities should formulatestandardized guidelines for the delivery of education over the internet and expand the types ofopportunities provided for instructors to receive adequate training.
Research on Equation \(\varphi\) (x)+2 = \(\varphi\) (x+2)

In this paper, we use the properties of Euler’s function, elementary methods and the idea of classification discussion to study the solvability of equation \(\varphi\) (x)+2 = \(\varphi\) (x+2)
Faulty Memories, Favored Outcomes: How Errors Impact Learning Processes

Recent studies suggest that errors facilitate learning in certain conditions. Despite this, reinforcement paradigms dominate learning methods, subscribing to the narrative that errorless learning is the foundation of an ideal learning environment. If we continue to view learning from this restrictive perspective, we may fail to capture and apply the benefits of errors. In this paper, we investigate two potential mechanisms of post-error learning. Participants (N = 61) learned word pairs in either a study or error trial before taking a final test. Supporting past error learning literature, errors before a study opportunity led to better performance on a final test. Differences in reaction times between conditions support the theory that errors increase learning by acting as a mediator, or secondary cue, to the correct answer on subsequent tests.
RANCANG BANGUN SISTEM INFORMASI ABSENSI KARYAWAN BERBASIS WEB (STUDI KASUS: PPKS MARIHAT DALU-DALU)

The implementation of employee attendance at the Marihat Dalu-Dalu Oil Palm Research Center (PPKS) still uses a manual attendance system for each employee. This can cause problems in the future If the data is lost or If some employees will misuse the data. Therefore, to ensure that employee attendance is used effectively and efficiently within the company, it is necessary to have a web-based employee attendance system that can be managed and not abused consciously by some employees when designing an employee attendance system. with the creation of this employee attendance system design, the development examples used for this information system are waterfall software development and use UML (Unified Modeling Language) diagrams, namely use case diagrams, activity diagrams, deployment diagrams, component diagrams. to form the design of this employee attendance system makes it easier for companies to manage employee attendance systems so that they can be organized and effective and efficient so that employee attendance data is not misused and damaged or lost.
An Ensemble-Based Model to Classify Scholarly Articles on Context: A Path to Recommender System

Articles from multiple research areas tend to use similar words as keywords and titles. On the other hand, selecting a publishing source can be problematic for authors in the initial stage of a scholarly study. So, context-based classification is important where the abstracts, keywords, and titles get similar attention. The aim of this study is to create a tool that uses ensemble learning to classify scholarly articles and recommends sources. This study uses 38 classes for the Web of Science dataset and 40 Classes for the Dimension dataset without grouping them. In all experiment setups models using abstracts achieved the best result as giving a more contextual understanding. Based on this ensemble-based approach, a recommender system has been outlined to recommend probable sources for a given article based on Title, Keywords, and Abstract.
A New Metric based on the Weighted Class Complexity (WCC) metric and measure of complexity for Object-Oriented Systems

Class-level complexity is a significant component of software complexity, and measuring software complexity is essential for software development and maintenance. Although the Weighted Class Complexity (WCC) metric has a long history of use, it has some drawbacks, including the equal weighting of complexity components and the exclusion of some complexity elements,. In this paper, we present a novel metric, the Enhanced Weighted Class Complexity (EWCC) metric, which extends the WCC metric by adding more complexity variables and weighting them according to their relative relevance. Data access patterns, method call chains, and control structure complexity are among the other variables. In order to compare the EWCC metric’s output to the WCC metric’s output, we apply it to a number of software systems. We assess the EWCC metric’s performance in locating potential error and bug sources. The findings indicate that, compared to the WCC metric, the EWCC metric offers a more thorough and precise evaluation of class-level complexity. According to the results, software developers and maintainers may find the EWCC measure to be a useful tool for controlling software complexity.
GAMBARAN KETERAMPILAN SELF REGULATED LEARNING SISWA SMP KELAS VIII SELAMA PEMBELAJARAN DARING

This study purpose to describe the self regulated learning skills of grade VIII junior high school students during online learning at SMP Negeri 9 Cimahi. This research is based on the reality at school, where there are a number of students who have a level of self regulated learning below the average while participating in online learning. The method used in this research is a qualitative method with a descriptive type of research. This study uses interview and observation techniques to collect data. The informants in this study were junior high school students grade VIII totaling 3 people with the criteria of having low self-regulated learning during online learning. In terms of metacognitive, motivational and behavioral aspects, the results of this study indicate that during online learning, students find it difficult to organize themselves in independent study sessions. This study also found internal factors and external factors as two factors that affect students' self regulated learning.Â Keywords: Online Learning, Self Regulated Learning
PENGARUH PEMAHAMAN SISWA DAN SISTEM PENILAIAN TERHADAP KETRAMPILAN BERBAHASA INGGRIS SISWA KELAS VI SD YAYASAN PENDIDIKAN JAYAWIJAYA KUALA KENCANA

English lessons at SD Yayasan Pendidikan Jayawijaya are introduced to students starting from grade I. The sixth-grade student feels they are skilled in speaking, listening, reading, and writing in English. But in reality, many students are having difficulties with English. When the researcher came into the English class, many students said that were not confident in speaking English. This study aimed to determine whether understanding ability and assessment systems will influence speaking, listening, reading, and writing skills in English. This research was conducted on sixth-grade students of Sekolah Yayasan Pendidikan Jayawijaya with a population of 65, using ex post facto research. The measurement of variables using a Likert Scale with multiple regression analysis. The instruments used are two tests, a questionnaire, and observation. The results of the analysis show that the variables of speaking, listening, reading, and writing skills in English are influenced by the ability to understand. The assessment system has the least influence.
Performance Assessment of a New Swahili Lexicon (SWAHILILex.01) Tagged by Native Speakers for Polarity Analysis

&lt;p&gt;This study tests the new Swahili Lexicon (SWAHILILex.01 ) annotated by native Swahili speakers for polarity analysis using pre-tagged datasets. The testing is against existing methods for polarity analysis that use lexicon-based methods, pre-trained models with transformers, and supervised machine-learning tools. The metrics for overall classification performance were accuracy, recall, precision, and F1-Score. The performance of the new SWAHILILex.01 lexicon was similar to the results of supervised machine learning and outperformed other methods when classifying the regular Swahili dataset but underperformed when classifying the tweets dataset. These preliminary results emphasize the need for domain-based Lexicons or new techniques that account for the multidomain experience common in social media data. The future research plan will expand SWAHILILex.01 to include other Swahili dialectics, extend polarity levels that focus on the emotional context, and create a pre-trained model for Swahili sentiment analysis for multidomain sentiment analysis. &lt;/p&gt;
Counterfactual Detection

Abstract Counterfactual statements, which describe hypothetical events that did not occur due to different circumstances, have been studied in various fields, such as NLP, psychology, medicine, politics, and economics. They can be misleading in productr eviews and are challenging to detect in multilingual setups due to language diversity. Current systems struggle to identify counterfactual statements due to limite ddata availability. To address these issues, a proposed counterfactual detection system utilizes a domain-independent and multilingual few-shot model. The system incorporates clue-phrases to mitigate the limited data problem and improve performance by 5-10% compared to existing few-shot techniques. The use of clue-phrases during training can enhance the efficacy of the model, making it more efficient at identifying counterfactual statements.
Uncovering demographic information on deep-face features

Facial recognition is one of the most successful applications of Deep Learning, with the advent of Convolution Neural Networks (CNN) being associated with break-through results in the last few years.The novel aspect of the use of CNNs has not changed the basic facial recognition pipeline, though: facial detection, pre-processing, feature extraction, and comparison/recognition.This paper investigates the possibility of inferring demographic information from facial features generated by CNNs trained for facial recognition.Pre-trained models on three different architectures (ArcFace, DeepFace and FaceNet) are used to extract features from faces of five distinct datasets: Fairface, UTKFaces, Labeled Faces in the Wild, Racial Faces in the Wild, and CelebA.Features and labels from the Fairface dataset are used to train neural networks classifiers for gender (female and male) and ethnicity (african, asian, caucasian and indian).Differences in the performance of the classifiers were observed, depending on which facial recognition model/ architecture is used as feature extractor.Some of the trained classifiers showed an improvement in performance compared to results in the literature and very low variance among demographic groups. PREVIOUS WORKSWe briefly review some works related to attribute analysis based on deep neural networks.In 2014, (ZHANG et al., 2014) hair style, clothes style, expression, action proposed a method that combines partbased models and deep learning by training pose-normalized CNNs to estimate various attributes, such as gender, hair style, clothes style, expression, and action, using the whole image as input.(LIU et al., 2015) introduced LNet+ANet in 2015, with a focus on facial attributes -it takes only facial images as input.This work also introduced two new datasets,
PEDAGOGICAL ESSENCE AND MODEL OF FORMATION OF INFORMATION AND TECHNICAL COMPETENCE IN APPLIED MATHEMATICS STUDENTS IN BACHELOR STUDIES

In the article, taking into account the possibilities of forming information-technical competence of applied mathematics bachelors of theoretical mechanics, its main didactic principles are defined, we reflect the essence of the educational process, that is, the theoretical approaches to the organization of the educational process and its management are revealed.
IPHFMs: Fast and accurate Polar Harmonic Fourier Moments

Polar Harmonic Fourier moments (PHFMs) are widely used in image processing due to their excellent reconstruction ability. However, PHFMs suffer from geometric errors and numerical integration errors. Also, direct computation of PHFMs from their definition is usually slow. This paper proposes a fast and accurate calculation method for PHFMs, named improved polar Harmonic Fourier moments (IPHFMs). The variable equidistant discrete approach and fast Fourier transform (FFT) reduce the geometric errors and time complexity. Extensive experimental results on a real-world application demonstrate the efficacy and superiority of the proposed IPHFMs, concerning speed and accuracy.
Relationship and Impact of Information Seeking Anxiety and Personality Traits on Health Information Seeking Behavior during COVID-19

Background: During covid-19 period not only general public was victim of anxiety besides all medical professional also face anxiety and change their Information seeking behaviour according their personality. Curiosity is in human nature with the easy access to internet the new horizon to information has been opened. People searching trends have shown that they are interested in health risk to health treatment for their health related problems. Aim: In this study examined the influences of anxiety (ISA) and Personality traits (PT) on health information seeking behaviour (HISB) among the Doctor, paraprofessional and final year medical students who are frontline worker during pandemic situation. Methodology: The study adopted survey method with non-probability convenience sampling to collect statistical. Questionnaires werefiled from 313 participants by utilizing convenient sampling and analyzing the data through SPSS. Results: The result showed that significant relation between personality traits, information seeking anxiety and health information seeking behaviour. In medical library user PT has significant impact on HISB (p&lt;.05), (AVG_PT=.002) and ISA has impact on HISB but it is not significant in medical professional (β -.070) value shows ISA has negative impact on HISB. Practical implication: This study will be beneficial for information professionals, health care workers, policy makers and administrators to access of information resources in hybrid format. Conclusion: Medical professional’s plays an important role in our society. They work hard and served the nation during pandemic situation. Anxiety is natural phenomena to every person. So medical professional also feel anxietybut the medical profession demands its professionals to stay cool, calm and free of anxiety by having analytical and cognitive skills, in order to fulfill the needs of their profession. This research helps to understand that ISA has no significant impact on HISB while PT has significant impact on HISB. Keywords: Information Anxiety, Health information seeking behaviour, Personality Traits
Research on Method of Creating Dynamic Weld of ROI Region Based on Faster-RCNN

Aiming at the issues of weld marking noise in welding path planning of the third generation welding robot, that the creation of ROI region is employed as the approach to noise suppression. However, traditional ROI region construction methods can only create ROI regions at a fixed location by presetting parameters in the system. The welding target position usually produces displacement in the control range of the tolerance due to an important tolerance concept in the welding process, which may result in an ROI region created with traditional methods is not able to coincide with the ROI region required by the system, thereby affecting the quality of the welding. To improve the location accuracy of the created ROI region, a dynamic ROI region creation method based on Faster-RCNN target detection algorithm was proposed. Experimental results show that this method effectively reduce weld marking noise.