Reviewerly/exHarmony huggingface dataset:
Topic Modelling in Computer Security Discourse: a Case Study of Whitepaper Publications and News Feeds

Up-to-date information plays a crucial role in modern linguistic research. For this reason,computational linguistic methods, including those aided with analytical and machine-learning tools, are attracting growing attention. Some of their applications in cognitive-discursive linguistics are keyword extraction, topic modelling, and content analysis. Text-mining tools facilitate time-consuming linguistic work andadd to the results’ reliability and greater statistical precision by processing a significantly larger data volume.Most studies, however, have overlooked interference of socially significant but context-irrelevant (e.g. political) information into a specialized discourse by focusing mainly on one data format. The current study,aimed at topic modelling, has been carried out on the computer security discourse. We have implemented theproject on the KNIME analytical platform. The model enables comparison between topics extracted frompublished articles and date-specific RSS news feeds. The study provides important insights into infodemiology and political incidental news exposure occurring in computer-security-oriented RSS feeds on theKaspersky website but untraceable in the papers published on the same website in a PDF format. The resultsreported here provide further evidence for the need to consider the hypercontext of professional communication and employ real-time data in solving similar problems within cognitive-discursive linguistics.Our contribution to the development of cognitive-discursive linguistics is the method for comparingtopics within one discourse, taking into account near-real-time data. For computational linguistics, the significance of our work lies in describing a new application of the topic extraction workflow freely available onthe KNIME hub.
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

Knowledge-based visual question answering (VQA) involves answering questions that require external knowledge not present in the image. Existing methods first retrieve knowledge from external resources, then reason over the selected knowledge, the input image, and question for answer prediction. However, this two-step approach could lead to mismatches that potentially limit the VQA performance. For example, the retrieved knowledge might be noisy and irrelevant to the question, and the re-embedded knowledge features during reasoning might deviate from their original meanings in the knowledge base (KB). To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA. Inspired by GPT-3's power in knowledge retrieval and question answering, instead of using structured KBs as in previous work, we treat GPT-3 as an implicit and unstructured KB that can jointly acquire and process relevant knowledge. Specifically, we first convert the image into captions (or tags) that GPT-3 can understand, then adapt GPT-3 to solve the VQA task in a few-shot manner by just providing a few in-context VQA examples. We further boost performance by carefully investigating: (i) what text formats best describe the image content, and (ii) how in-context examples can be better selected and used. PICa unlocks the first use of GPT-3 for multimodal tasks. By using only 16 examples, PICa surpasses the supervised state of the art by an absolute +8.6 points on the OK-VQA dataset. We also benchmark PICa on VQAv2, where PICa also shows a decent few-shot performance.
The “Moscow Case” as a Factor of Protest Communication Activity in the Social Network “VK”

This paper addresses the impact of negative juridical sanctions implemented against the participants of the 2019 protest movement in Moscow on the communication in the social network “VK”. The empirical analysis of the mentions of protests and juridical prosecutions allowed to estimate the volume of discussions about the negative sanctions, to compare it with the discussions around the protest rallies and to outline the main factors affecting how active the discussion is on different episodes of negative sanctions. The results show that the coverage of the criminal persecution of activists provokes substantively less interest among the authors and the readers than the communication on the protest movement itself. Increased activity in comments is not observed, while the communication is concentrated in large communities. The input in the protest communication was not the same for sanctions against different activists: the impact was more notable when the media campaign in defense of the accused managed to involve persons with high media capital or a considerable number of people outside the protest movement. In the cases when the events around the accused were only relevant for other activists the surge in online communication turned out to be less noticeable. This implies that negative juridical sanctions are not a newsbreak themselves but have the potential to provoke an outburst in protest communication if the coverage is complemented by the traditional forms of political mobilization or includes opinion leaders.
Correction: Hayat et al. Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches. Information 2022, 13, 275

In the original publication [...]
Pre-processing optimization for learning power consumption prediction models

Recently, it is trying to realize zero carbon through energy management based on power consumption prediction. To this end, research is being conducted to predict power consumption using a deep learning model. Data pre-processing is essential for deep learning model learning. However, there is a problem that such optimization works according to empirical design. Genetic algorithms are suitable for these optimization tasks. We propose a data optimization method using genetic algorithms. We learned the RNN model using optimized data and derive up to 83% higher performance than the heuristic method-based RNN model.
ESTIMASI PENGUKURAN PERANGKAT LUNAK MENGGUNAKAN METODE MATRIK FUNCTION POINT ANALYSIS – STUDI KASUS APLIKASI AKADEMIK SISWA BERBASIS WEBSITE

Function Point (FP) is a measurement method as well as a unit of measure from a software. FP can measure software by quantifying (number of units in numbers) functionality in software that is already provided to users based on logic. The FP matrix is calculated from 5 components which include the number of user inputs (external input-EI), the number of outputs to the user (external inquiry - EQ), the number of user inquiries (external inquiry - EQ), the number of internal files used (internal logical file - EQ). ILF), and the number of external interfaces (external files – EIF). This case study aims to study carefully how to calculate FP which produces the final result in the form of cost estimation in software. The existing software is the Website-Based Student Academic Application (AASBW). The technical files available from this software are source code and running application. This FP measurement will be carried out from a software point of view, namely measuring the finished application. From the observation, it is found that the FP for AASBW = 152.90 and the estimated cost to market this application is Rp. 7,645.00, the estimated production time is 29 days, and the estimated cost of managing this application is Rp. 3,850,000, so the results of the three estimation factors are used by the authors as analysis material for Website-Based Student Academic Applications using the Function Point Analysis method.
FORECASTING WITH WEIGHTED MOVING AVERAGE METHOD FOR PRODUCT PROCUREMENT STOCK

ABSTRACTDhanty Store is a family start-up located in East Jakarta. It was initiated in 2018, engaged in retail with the main product in the form of women's clothing and accessories. One of the important processes in Dhanty Store operations is the product procurement process. Currently, Dhanty Store request products according to their wishes without looking at their sales data. This causes their product stock is not well controlled. When there is a lot of demand, sometimes Dhanty Shops run out of stock so their customers will move to other stores. In addition, the process of requesting and procuring products to suppliers also takes a long time so that it can further disrupt the operations of Dhanty Store. This study develops a forecasting application prototype with the Weighted Moving Average method to assist Dhanty Store in the process of requesting and procuring their products. Forecasting results in the period (t) of the 1st week of January were 275 products. In addition, this study predicts product stock with a 4-week moving average and the MAD tracking signal value is ranged from -1.51 to 3.86 and the MAPE value is 35.4%. As for the reliability and level of user acceptance of the prototype model in this study, tested using the System Usability Scale (SUS) method and it is known that the average value given by respondents was 82 with details 0% considered inappropriate, 40% considered neutral and 60% rated it according to need. Keywords: data mining, forecasting, weighted moving average, MAD, MAPE, SUS
An Investigation on the Safety Management Policy and Characteristics of Domestic Plasma-derived Medicinal Products

Plasma-derived medicinal products (PDMPs) are pharmaceuticals prepared using proteins in plasma, a liquid component of blood. Regarding the use of human blood as a raw material in PDMP manufacturing, it has unique characteristics unlike other pharmaceuticals (for example, limitations of scientific identification, finiteness of blood, and risk of infection through disease pathogens, such as the human immunodeficiency virus, hepatitis B virus, and hepatitis C virus in the blood). The safety management policy of PDMPs in Korea started with the enactment of the Blood Management Act in 1970. After that, the current safety management system was established through product approval management according to the Pharmaceutical Affairs Act in 2006, higher standards of safety management for imported source plasma in 2009, and expansion of management standards for source plasma in 2012. The safety management of PDMPs involves several important policies. Public management has been carried out by the Korean Red Cross and national government, and domestic plasma should be used first, while imported plasma may be used only if there is not enough. Additionally, to ensure blood products safety, plasma testing, inventory hold, and plasma master file reporting obligations are being implemented. In addition, it is managed as a subject of national lot release, which should also be quality-confirmed by the country. Because the supply of domestic source plasma, which is the raw material of PDMPs, has shown a decreasing trend in recent years, continuous monitoring and preparation is required. In this study, we describe the history of safety management policies, including the definition and classification of PDMPs, product status, usage status of source plasma, and the related policy guidelines and characteristics.
LEARNING MODEL METHODS EMOTIONAL DEMONSTRATION (EMO DEMO) IN PREVENTION OF NON-COMMUNICABLE DISEASES: QUASI-EXPERIMENTAL STUDY

Background. The development of science and technology in the field of medicine encourages experts to always conduct research on various diseases, including one of them is the incidence of infectious diseases in order to overcome suffering and death due to these diseases.This study aimed to analyze the effectiveness of the emo demo method in increasing knowledge and skills about the prevention of non-communicable diseases. Methods. This quasi-experimental study using two groups of pretest-posttest design involved 100 participants, whose data were collected using a questionnaire and tested using the Wilcoxon test. Results. Both knowledge and skills variables show differences before and after the intervention of providing education, namely there is an increase in knowledge and skills to prevent non-communicable diseases. There are different mean values ​​between pre-test and post-test knowledge and skills, meaning that mathematically indicates there are differences in knowledge and skills before and after Emo Demo. Conclusion. The Demonstration Emotional Method learning model is effective in increasing students’ knowledge and skills about preventing non-communicable diseases.
The Effect of Different Similarity Distance Measures in Detecting Outliers Using Single-Linkage Clustering Algorithm for Univariate Circular Biological Data

The procedure of outliers detection in univariate circular data can be developed using clustering algorithm. In clustering, it is necessary to calculate the similarity measure in order to cluster the observations into their own group. The similarity measure in circular data can be determined by calculating circular distance between each point of angular observation. In this paper, clustering-based procedure for outlier detection in univariate circular biological data with different similarity distance measures will be developed and the performance will be investigated. Three different circular similarity distance measures are used for the outliers detection procedure using single-linkage clustering algorithm. However, there are two similarity measures namely Satari distance and Di distance that are found to have similarity in formula for univariate circular data. The aim of this study is to develop and demonstrate the effectiveness of proposed clustering-based procedure with different similarity distance measure in detecting outliers. Therefore, in this study the circular similarity distance of SL-Satari/Di and another similarity measure namely SL-Chang will be compared at certain cutting rule. It is found that clustering-based procedure using single-linkage algorithm with different similarity distances are applicable and promising approach for outlier detection in univariate circular data, particularly for biological data. The result also found that at a certain condition of data, the SL-Satari/Di distance seems to overperform the performance of SL-Chang distance.
Fire Vulnerability Assessment using Multicriteria Analysis in Makassar City

The safety aspect, especially the fire disaster, is essential for Makassar City because of its role as a metropolitan city and a center of activity in the Eastern Indonesia Region. The dense population and activities make Makassar City vulnerable to fires. A vulnerability assessment can assist in urban disaster management, especially in highlighting areas of fire disaster mitigation.Based on this urgency, this study aims to identify fire-prone areas in Makassar City. This study examines the fire vulnerability of Makassar City from population density, building density, frequency of previous fire events, fire fires, distance to the availability of clean water, and dangerous buildings. Fire susceptibility criteria are based on stakeholder assessments involving disaster experts, city planners, and firefighters through the Analytical Hierarchy Process. Spatial assessment using multi-criteria analysis through the Simple Additive Weighting method, which is integrated with the Geographic Information System to allow spatial weighting. The study results show that the western and northern parts of Makassar City are very prone to fires. This area is an early development area for Makassar City, characterized by a dense population and buildings with various activities.
Study Interest Analysis on Indonesian Language Subjects

This study aims to analyze interests in the Indonesian Language subject. This study uses a descriptive analysis method using a qualitative approach and tests the hypothesis using the interpretation of the percentage value. This study found that interest in learning Indonesian increased significantly; this is evidenced by the claim that students have in learning Indonesian, both individually and in groups. Alternatively, in other words, taking part in Indonesian language exercises that are beneficial for students in Indonesian subjects—increased because it is influenced by factors of motivation and the state of the school environment, and the role of the teacher.
New Ways to Improve Student Material Understanding Using Quizizz During the Covid-19 Pandemic

Covid-19 has had a considerable impact on learning, so to keep improving student learning outcomes, lecturers are required to be more creative in using technology applications such as quizizz. This study aims to see the effectiveness of quizizz in improving student learning outcomes during the implementation of online learning during the Covid-19 pandemic. This type of research is quasi-experimental, involving two sample classes, namely the experimental class and the control class. Sorting the two sample classes using random sampling technique. The data collection instrument used multiple choice tests. The data analysis technique used the t test by first doing the prerequisite tests, namely normality and homogeneity tests. The results of data analysis show that there are significant differences in learning outcomes between students who study using Quizizz and study conventionally. Quizizz has contributed significantly to student learning outcomes, because quizizz is a learning application that is packaged in an attractive manner, has a good graphic design and is automated. Online learning should integrate one of the quizizz applications so that it can motivate students to learn and can directly improve student learning outcomes. The creativity of the teacher in operating the quizizz also influences the course of the learning process.
Development of Adobe Flash-Based Multimedia in Learning Recognition of Parts of Human Body

The learning process in the classroom is difficult for students to understand the material being taught, because learning does not apply learning material in using media in the classroom environment, so the learning process in class is not effective in understanding the material being taught. This study aims to identify a description of the need for developing multimedia learning of human limb recognition using Adobe Flash, designing multimedia for learning human limb recognition using Adobe Flash, measuring the level of validity and practicality of multimedia for learning human limb recognition using Adobe. This research is a type of RD (Research and Development) used ADDIE model (analysis, design, development, implementation, evaluation). The results showed that Adobe Flash-based multimedia products were valid to be used based on the results of validation by content/material experts obtaining a percentage result of 100% being in very good qualifications, validation results by design experts obtaining a percentage of 86% being in good qualifications, validation results by media experts obtained a percentage result of 94% being in very good qualifications, then in practicality trials conducted on students who obtained results 98.17% were in very good qualifications, and the responses of subject teachers related to Adobe Flash-based multimedia obtained 100% results which were in very good qualification. Based on the results of the analysis, it can be concluded that Adobe Flash-based multimedia is valid and practical to use in the learning process of Education, Physical, Sports and Health subjects.
Strengthening School Literature Movement in Elementary School During the Covid-19 Pandemic

Literacy development in Indonesia is still reported to be low. This is written in the findings of a 2020 program for international student assessment (PISA) survey, which found that Indonesia ranked 72 out of 79 countries in the world in reading literacy. During the Covid-19 pandemic, the implementation of the school literacy movement program was done online. The purpose of this study is to investigate the implementation of SLM throughout the Covid-19 era, as well as the barriers encountered by teachers during the SLM implementation process. The case study method is used in this work, which takes a qualitative approach. Data was collected by observation, interviews, and documentation, and data was analyzed utilizing triangulation techniques in three stages: collection, reduction, and conclusion. The findings revealed that schools were still practicing the three steps of SLM, namely habituation, development, and learning. During the epidemic, literacy exercises are introduced in which students are directed to be productive in their work through writing tasks. The work of the students is published in the form of a book. The challenge to implement SLM during the Covid-19 pandemic has been teachers' inability to manage students' reading comprehension exercises properly.
Feasibility Research of Teaching Materials of Appreciation Study of Fiction Prose Based on Makassar Local Wisdom

The purpose of this study is to create teaching materials for the study of prose fiction based on Makassar local wisdom that are appropriate or valid for students of the Indonesian Language and Literature Education Study Program at the Muhammadiyah University of Makassar. This type of research is research and development or Research and Development (RD) using the ADDIE model (Analyze, Design, Develop, Implement, Evaluation). The subjects of this study were two experts (experts) who assessed the teaching materials. Data collection techniques include test techniques, questionnaires, observations, interviews, and documentation. The data analysis technique used descriptive qualitative analysis techniques and descriptive statistics. Based on the results of this study, it was found that teaching materials for the study of prose fiction based on Makassar local wisdom were feasible or valid in terms of the elements of teaching materials (4.32) or with a very decent category, presentation elements (4.07) or with appropriate categories, graphic elements ( 4,33) or with a very decent category, and linguistic elements (4,19) or with a decent category.
Children’s Handicaps in Online Learning: A Highlight on Learning Applications

This current study aims to document the challenges encountered by children in adapting to the learning applications in online learning. A literature study is used to answer the research question. The data was obtained from a systematic search in the Google scholar. From the search, 23 articles from 2000 – 2021 were gathered as the research data. To analyze the data, a content analysis was implemented. The findings revealed that the children met the difficulties because they tend to have short concentration span and the limitation of learning device and signal stability. Moreover, less parents’ control causes young learners disoriented while using the applications. Surprisingly, the students did not face hindrance in relation to the applications use. Most of them adapted quite easily in using the applications. This implies that children nowadays are very adaptive towards technology and are easier to learn through diverse platforms in online learning. Limitations and future recommendations are also presented.