Reviewerly/exHarmony huggingface dataset:
Robust Feature Selection by filled function and fisher score

Feature selection is essential in high-dimensional data analysis and filter algorithms, and due to their simplicity and fast speed, they have increasingly been drawing attention in recent years. Retaining all features in machine learning tasks is not only inefficient but the irrelevant and redundant features may have an adverse impact on the classification accuracy rate. Feature selection is an optimization problem which aims to transform the dataset’s high-dimensional space to a lower-dimensional space by utilizing the relevant and suited features. Feature selection is a time-consuming task, while it is very effective in saving the time devoted to the learning algorithm. In feature selection algorithms, filter algorithms are increasingly attractive due to their simplicity and fast speed. In this paper, we are going to introduce a supervised filter feature selection using filled function and fisher score (FFFS). Based on this criterion, we try to find a feature subset resulting in the least classification error rate. In order to prove the effectiveness of the proposed algorithm, Extensive experiments have been conducted on 20 high-dimensional real-world datasets. Experimental results reveal the superiority of the proposed algorithm to state-of-the-art algorithms in terms of minimum classification error rate. Results validated through statistical analysis indicated that the proposed algorithm is able to outperform the reference algorithms by minimizing the redundancy of the selected features. So, the selected feature subset can avoid serious negative impacts on the classification process in real-world datasets. In addition, this paper proves the ability of the proposed algorithm in selecting the most relevant features for classification tasks by applying different noise rates to the datasets. According to the experiments, the FFFS is less affected by noisy attributes in comparison with other algorithms. Thus, it is a reasonable solution in handling noise and avoiding serious negative impacts on the classification error rate in real-world datasets.
PENERAPAN QR-CODE DALAM PROSES PEMBAYARAN DIGITAL DI KAMPUS

&lt;p&gt;&lt;em&gt;The payment system at Dayanu Ikhsanuddin University (Unidayan) is done manually by taking a payment slip at the financial counter, then making payments at the designated bank, such as payment of tuition fees, KKN, graduation, PBL, PPL, and final project or thesis. Payment methods like this make students often experience difficulties in making payments, causing discomfort and tend to delay payments. This study aims to apply the QR Code system in the digital payment process on campus. The research method used is the waterfall model. The method implements a sequential software life flow, starting from analysis, design, coding, and testing. This research produces a digital payment application on the Unidayan campus by implementing a QR Code system which can later make it easier for students to make payments by making a top-up and then scanning the QR code. thus it can help the finance department get data on students who have or have not paid tuition fees, namely SPP, KKN, Graduation, PBL, and Final Projects or Thesis.&lt;/em&gt;&lt;/p&gt;
SISTEM INFORMASI GEOGRAFIS BERBASIS ANDROID KANTOR DINAS PEMERINTAH KOTA BAUBAU

&lt;p&gt;&lt;em&gt;The Baubau City regional office is one of the regional apparatuses as an assistant element to the mayor and the Regional People's Representative Council (DPRD) in the administration of government affairs which are the regional authority of Baubau City. The Baubau City government office has 23 service offices spread throughout the Baubau City area with their respective roles and duties. The spread of office locations can make it difficult for the people of Baubau City and immigrant communities to find the location of the official office and information on the official office. Therefore, a geographic information system for mapping the Baubau City government office is needed. This study aims to design an android-based geographic information system application for mapping the Baubau City government office. The data used in this study is collected data on maps of the Baubau City area using Mapbox and leaflets js, interviews at each office address, collecting data on coordinate points, and some other supporting data. This research produces an application that can make it easier for users to find the location of official offices and display office information in the form of office names, addresses, coordinate points, telephone numbers, emails, websites, office pictures, and routes to the Baubau City government offices.&lt;/em&gt;&lt;/p&gt;
Simulation of the Implementation of Nursing Discharge Planning to Reduce Patient Recurrence Rates in Hospitals

Discharge planning is part of the nursing process and the main function of care that must be carried out by nurses in a structured manner starting from the time the patient enters the hospital. Discharge planning is a regulatory process that helps clients prepare to leave a better level of care, both inside and outside their health facility with the aim that patients and their families can manage post-discharge care independently. The purpose of discharge planning focuses on providing health education to patients to discuss nutrition, drugs, activities or training and special instructions regarding the signs and symptoms of the patient's illness. Before going home, patients and families are given education to be able to manage their condition and/or during recovery. Discharge planning in accordance with the standards must be carried out in the hospital, because discharge planning is an important element in nursing services which aims to make the client independent after leaving the hospital. The method of this community service is the counseling method in the form of direct presentation of lecture material, direct simulation and measuring the level of knowledge of nurses in evaluating the application of nursing discharge planning in order to reduce the recurrence rate of patients in hospitals with the number of nurses in this study namely 28 people.
Machine learning-based Fake reviews detection with amalgamated features extraction method

Product fake reviews are increasing as the trend is changing toward online sales and purchases. Fake review detection is critical and challenging for both researchers and online retailers. As new techniques are introduced to catch the fake reviewer, so are their intruding approaches. In this paper, different features are amalgamated along with sentiment score to design a model that checks the model performance under different classifiers. For this purpose, six supervised learning algorithms are utilized to build the fake review detection models, utilizing LIWC, unigrams, and sentiment score features. Results show that the amalgamation of selected features is a better approach to fake review detection, achieving an accuracy score of 88.76%, which is promising compared to similar other work.
Cryptocurrencies Analysis using Web interface

Since the early 20th century, when the internet first emerged, it has developed dramatically. People have been developing websites and software to produce and share content in the form of text, audio, and video, which is now referred to as social media. People are more informed about what is happening in the world today thanks to the internet. News, important updates, and communication are all done through various online applications; among them, Twitter and Reddit are well-known for having user bases of more than 500 million. With such a sizable community, individuals have a lot of influence over how society is changed. Cryptocurrency is referred to as "the money of the Internet," much like social media's "people of the Internet." Cryptocurrency is not issued by a bank or the government and does not exist in physical form like paper money. The value of cryptocurrencies might fluctuate greatly. As of 2022, there are more than 9000 cryptocurrencies available on the market, and more than 70 currencies have a market capitalization of more than $1 Billion. Some currencies can be hard coded to a specific value, while others have value dependent on its market cap, drawing a lot of investors. Millions of transactions worth billions of dollars are made every day on various exchange websites.
An Empirical Study of Supervised and Active Learning Methodologies for Classifying Research Papers

Document classification is one of the predominant tasks in Machine Learning, that by and large involves the use of supervised methodologies to determine most suitable categories for a given set of documents. In this project, we seek to conduct an empirical study, that, in addition to the conventional machine learning methods, experiments with active learning and semi-supervised methods for assorting a mix of labelled and unlabelled computer science scholarly articles hosted on the arXiV repository. We develop a solution using the gathered labelled data points, by experimenting with various multi-class supervised learning models such as K Nearest Neighbours, Naive Bayes, Random Forest, and Logistic Regression. We also account for the issue of class imbalance, and thus conduct experiments to determine a suitable oversampling methodology for addressing this concern. Lastly, we extend beyond the conventional machine learning classification methods to incorporate the concept of active learning, a type of iterative supervised learning, used when the unlabelled data is in abundance.
The Effect of Short and Long Fiber on Impact Strength in High Density Polyethylene-Fiberglass Composite

This study aims to investigate the HDPE (High Density Polyethylene) reinforced with fiberglass to increase strength and toughness into a composite that has the potential to replace ABS (Acronitrile Butadiene Styrene). The composites were made with variations in composition based on volume fraction. The HDPE as matrix and fiberglass as reinforcement through the mixing method, molded in a heating machine (T=140°C), pressed with a pressing time of ±45 minutes. Then, the specimens were cut according to ASTM D6110 standard, and the Charpy impact test was performed. The impact of long fiber for the HDPE composition of 70%vol and fiberglass 30%vol, the average impact strength was 176.838 Joule/mm². The long fiber for the HDPE composition of 80%vol and fiberglass of 20%vol, the average impact strength was 208.08 Joule/mm². The impact of short fiber for HDPE composition of 70%vol and fiberglass of 30%vol, the average impact strength was 72.858 Joule/mm². The short fiber for HDPE composition 80%vol and fiberglass of 20%vol, the average impact strength was 33.394 Joule/mm². Based on research of ABS mixture at 40%/60%vol mixture variation, the average impact strength was 24.8 Joule/mm2 and the 20%/80%vol mixture variation, the impact strength was 18 Joule/mm². In conclusion, the impact strength of the ABS was lower than the HDPE-Fiberglass composite. Therefore, the HDPE-Fiberglass composite can be used as a substitute for ABS for car bumpers.
Effect of Rotational Speed on Hardness Value and Area of Vertical Bar-Plate Rotary Friction Weld Joint

This study aims to determine the effect of rotational speed on the weld joint area and the hardness value of vertical bar-plate friction welding on dissimilar materials. Several testing methods were carried out, namely liquid penetrant, macro-observations, micro-observations and hardness test to investigation the welding results. The results of the liquid penetrant test have no effect on the welding specimens. Based on macro-observations was revealed a large enough cavity at a speed of 2.484 rpm with a cavity length of 4.76 mm, then getting smaller at a speed of 2.613 rpm with a cavity length of 2.63 mm. Then, at a speed of 4.335 rpm, no cavities were found. The micro-observation was a change in the microstructure, where in the weld metal area produces fine grains that affect the hardness value. The hardness value increases as it approaches the weld area. The highest hardness value at a speed of 4.335 rpm with a hardness value of 148.10 VHN, while the lowest hardness at a rotational speed of 2.484 rpm has a hardness value of 140.44 VHN.
Identification of Signature Images with Edge Detection Canny

In authenticating and verifying important documents, one of them is in the form of identifying the authenticity of a signature. In addition, the signature is also a form of ratification and a sign of approval in important documents is mandatory. Along with current technological developments, the signing process can be carried out in digital media such as cellphones and other media. The ability of the system to identify a person's signature becomes important because of the many forgeries that occur. This study aims to implement the Canny edge detection method to identify a person's signature. The number of signature images used is 10 signatures. The results of this study indicate that the Canny edge detection method has a similarity percentage of 70% to 100%, and the similarity values below 70% and above 100% are grouped into signature images that are not original.
Sensing Mental States: Way Forward

Flexible electronics has enabled to us reach newer heights. Now, has come the era of wearables. With the advent of advanced fabrication and integration technology, wearables have entered every domain of our life. The ease of use as well as minimal power requirement attract consumer to adopt them more and more as a part of their daily wear regime. These wearables provide direct monitoring many health issues such as sugar level, oxygen level as well as heart pressure etc. Recently, wearables have achieved another feat of sensing the mental state of the wearer. This short communication dwells upon this wearable providing information about the mental health. Along with working principle, the future perspectives are also provided.
Indonesian people’s resilience detection method based on big data

The resilience condition of the Indonesian people in facing threats, disturbances, obstacles, and challenges (AGHT) can be identified through conversations on social media. The conversational data of social media users is important data for understanding the national resilience of the Indonesian people. The method developed is more explorative, descriptive, and quantitative by describing the variable: volume of social media users, user profiles, reach, conversation trends, types of issues, top tweets, emotion, sentiment, people who influence (top influencer), intermediary (bridge), and robot analysis (bot analysis). The research sample is from March 1, 2022, to May 1, 2022. Consideration of the timing is due to many public reactions to the “three periods” issue. The results of this study indicate that the three-period issue is the most dominant compared to other topics. The issue of “three periods” spread throughout Indonesia, and the most dominant was in DKI Jakarta Province. The social media users’ profile shows that the issue of three periods is mostly discussed by users between the ages of 19 and 29. Men are more dominant in discussing the “three periods” issue than women. Most Indonesian people reject the three-period issue. It shows that the resilience of the Indonesian people is exceptional because they can confront negative issues.
Activities of the student association as a contribution to the development of professional skills of students and the beginning of scientific research

In 2022, the Professional Student Society of the RUDN University Ecology Institute turned 10 years old. The Society has made a significant contribution to the environmental education of many generations of environmental students. Many interesting expeditions, volunteer work, quests, round tables, reports were organized. Based on the results of field work, scientific articles and graduation papers were written, agreements were signed with national parks.
Cognitive Outcome Attainment of The Undergraduate Civil Engineering Students before and During COVID-19

This paper describes the performance of cognitive outcomes attainment of the undergraduate diploma in civil engineering students at Universiti Teknologi MARA (UiTM), Johor Branch, Pasir Gudang Campus.Specifically, this study examined the different cognitive outcomes attained among this population from before until during the implementation of the Movement Control Order (MCO) starting in the March 2020 semester.The study utilised data from the Programme Outcomes (POs) analysis by the Outcomes Based Education Committee every semester.This study identified different cognitive outcome attainments both before and during the COVID-19 semesters among students.The attainments used for the students are based on the Averaging Model, in which all courses contributing to the achievements are considered in the calculation of the cognitive outcomes average.The increase in percentages recorded from the result indicates that the shift to online learning has a useful effect on students' performances in cognitive attribution.Findings also show that some assessment grades, such as assignments, quizzes, and tests during COVID-19 semesters, can possibly improve greater achievements in cognitive level among students.The study discusses the implementation and functional descriptions of the findings and how they would be beneficial to stakeholders.
Multimedia Approach (Geo-Sirah) Development on Sirah Learning with Geography Elements: Primary School Students

Learning Sirah has a sequence of incidents, locations and dates that need to be recognised and understood during the journey of a particular story through reading.Most students easily get distracted quickly when engaged with much reading and lack the desire to learn only using traditional teaching methods.Therefore, the best alternatives should be applied in teaching and learning to stimulate and enhance students' interest in learning Sirah with the integration of multimedia and geographical element such as location, distance, coordinate, and direction.Thus, this study aims to enhance a prototype of the Geo-Sirah application for primary school students and obtain users' acceptance of the proposed Geo-Sirah prototype.ADDIE model was adapted to develop the prototype.The results showed that the mean score of perceived ease of use was higher than other criteria with 4.47, followed by intention to use the application (mean score = 4.39).The mean score obtained on perceive usefulness was 4.38 and attitude was 4.36.It shows that perceived ease of use, and intention to use the application in Sirah subject positively impacted user acceptance and interest in using the application to learn Sirah subject with the geography element through animated storytelling.
English Reading Difficulties and Strategies for Improvement at Primary Level: A Phenomenological Study

English is an alien language. The teachers and students are facing difficulties in reading competency regarding, comprehension, speed, shyness, stuttering and stammering, jerks, fluency, pronunciation, accent intonation, and stress. The purpose of the study is to explore the difficulties in English reading competency and to develop strategies for the improvement of reading competency at the primary level. The key objectives of the study were to explore the difficulties in reading competency and to develop strategies for the improvement of reading competency of 5th-grade students at the primary level. These research questions were framed. What are the difficulties in reading competency faced by 5th-grade students? What strategies are needed to develop the reading competency of 5th-grade students? The study was qualitative and phenomenological in nature. The study was carried out through interviews with primary-level teachers and 5th-grade students. The sample was 12 participants, 6 were teachers and 6 were 5th-grade students. The analysis of the gathered data was made with the thematic analysis of Clarke and Braun (2019). Major findings were that students’ lack of interest, mal-recruitment of engineers, MBA degree holders and lack of relevant educational background, adhocism, contract appointment and hiring teachers’ trend, poor and uneducated parents and incompetency of teachers in the subject of English were the key difficulties in reading competency. These strategies were recommended. The teachers may be recruited who have Master's degrees in English with educational qualifications like B.Ed., M.Ed., MPhil, and Ph.D. in English or education. It was recommended that English Language teachers must provide basic reading skills to students to equip themselves with reading strategies for improvement in reading skills. It is also suggested that teachers may be given refresher programs through English experts for the improvement of reading competency.
Regulation of Copyright Translation of Literature on Digital Platforms: A Comparative Study

The emergence of digital literature as an object of digital copyright is one of the phenomena of the development of information technology. However, unlimited access to digital containers can make it easier for someone to misuse a work of digital literature against rights. Each country, through its national laws, then tries to provide legal protection for the parties, including the United States through the Digital Copyright Millennium Act (DMCA) and Indonesia through the Copyright Act 2014 (UHC). This research will discuss the regulation of digital literature translation contained in the DMCA and UHC. This research is juridical-normative with the case, statutory, and comparative approaches, whose data collection is done through a literature study. The results of the study concluded that, First, the DMCA has comprehensively regulated the provisions of digital copyright so that it has provided legal certainty. In contrast, the UUHC has not been regulated completely, so it does not provide legal certainty. Second, the DMCA and UUHC are still unable to accommodate the ideal legal protection of digital literature, and Third, the provisions of the DMCA and UUHC also still do not provide legal benefits from the regulation of digital copyright.
Benchmarking gradient based Optimizers' sensitivity to Learning Rate

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.
Adaptive Fine-tuning for Multiclass Classification over Software Requirement Data

Abstract The analysis of software requirement specifications (SRS) using Natural Language Processing (NLP) methods has been an important study area in the software engineering field in recent years. Especially thanks to the advances brought by deep learning and transfer learning approaches in NLP, SRS data can be utilized for various learning tasks more easily. In this study, we employ a three-stage domain-adaptive fine-tuning approach for three prediction tasks regarding software requirements, which improve the model robustness on a real distribution shift. The multi-class classification tasks involve predicting the type, priority and severity of the requirement texts specified by the users. We compare our results with strong classification baselines such as word embedding pooling and Sentence BERT, and show that the adaptive fine-tuning leads to performance improvements across the tasks. We find that an adaptively fine-tuned model can be specialized to particular data distribution, which is able to generate accurate results and learns from abundantly available textual data in software engineering task management systems.