Reviewerly/exHarmony huggingface dataset:
Guidelines for agile concurrent product development in SMEs

Constantly changing project environment and a new paradigm shift to a more human-centred and sustainable Industry 5.0 has also brought about a need for a new project management style. Over the last decades, more flexible and people-oriented agile project management (APM) approaches have emerged. Due to its many benefits, APM has already become a mainstream in software industry, and is now increasingly being transferred to other industry sectors. In this paper, we present some basic guidelines for manufacturing SMEs on how to increase their agility without having to undergo a complete agile transformation that requires high financial and non-financial resources. The guidelines are based on three key pillars: concurrent product development, APM, and modern technologies, and are focused on achieving both high project efficiency and stakeholders' satisfaction.
Learning with Instance Bundles for Reading Comprehension

When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related questions and their corresponding answers are not independent, and leveraging these relationships could provide a strong supervision signal to a model. Drawing on ideas from contrastive estimation, we introduce several new supervision losses that compare question-answer scores across multiple related instances. Specifically, we normalize these scores across various neighborhoods of closely contrasting questions and/or answers, adding a cross entropy loss term in addition to traditional maximum likelihood estimation. Our techniques require bundles of related question-answer pairs, which we either mine from within existing data or create using automated heuristics. We empirically demonstrate the effectiveness of training with instance bundles on two datasets—HotpotQA and ROPES—showing up to 9% absolute gains in accuracy.
Thermal energy data monitoring system based on data collection of thermal metering equipment of internet of things

To solve the inaccurate measurement of the traditional thermal energy network, the paper designs a thermal energy network monitoring and control system based on GPS and Ti3367 wireless transmission and reception based on the IoT. First, the paper designs the monitoring and control system?s overall function and topology, including the management layer?s complex functions, the data aggregation layer, and the data acquisition layer. The paper then designs the system?s hardware structure based on the IoT, including the connection design of the hardware circuit structure diagram of the data aggregation layer and the data acquisition layer. Finally, the paper realizes the system?s software flow design, including system initialization and wireless data receiving and sending flow design.
Андрагогічний підхід у післядипломній освіті викладачів іноземних мов вищих військових навчальних закладів та військових навчальних підрозділів закладів вищої освіти

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Analysis of the spelling, effective sentences, and paragraphs in the students scientific papers

This study aims to determine the level of use of spelling, effective sentences, and Indonesian language paragraphs in scientific papers applied by undergraduate students of the State Polytechnic of Bali in the form of theses. This research is important because there is a tendency to use spelling, effective sentences, and paragraphs in student scientific papers that are not in accordance with applicable rules such as prepositions, punctuation. The method used in this study is a qualitative method, while the method of data collection in the form of documentation with qualitative descriptive analysis. Studies on spelling order, each of them scored: Managerial Accounting Study Program 98.73, International Business Study Program 98.96, Tourism Business Management Study Program 99.02, Construction Management Study Program 98.51 with an average rating of 98.80. At the sentence level each gets a value: Managerial Accounting Study Program 83.72, International Business Study Program 85.22, Tourism Business Management Study Program 77.25, and Construction Management Study Program 74.75, while at the paragraph level each value: Accounting Study Program Managerial 70.74, International Business Study Program 61.20, Tourism Business Management Study Program 60.31, and Construction Management Study Program 66.87, with an average rating of 64.78. Thus, it can be concluded that spelling ability, effective sentence, and student paragraphs applied bachelor of Polytechnic Bali is very good.
Implementasi Metode Penghalusan Ekponensial Tunggal Dalam Prediksi Penjualan Buku

The prediction of the quantity of product sales in the future is intended to control the amount of existing product stock, so that product shortages or excess stock can be minimized. When the quantity of sales can be predicted accurately, the fulfillment of consumer demand can be sought on time and the cooperation of the store with the relationship is maintained well so that the store can avoid losing both sales and consumers. The purpose of this study is to compare the effectiveness of the use of the Single Exponential Smoothing method and methods Double Exponential Smoothing with a smoothing parameter value a = 0.5 for forecasting sales by comparing the error values in the two methods using the Mean Squared Error (MSE) method, the MSE results of the Single Exponential Smoothing method is 4967.75 while the MSE Double Exponential Smoothing is 5113.03. Thus, the Single Exponential Smoothing method is more accurate than Double Exponential Smoothing in calculating book sales forecasting because it has a low MSE value.
Developing an e-module of making Edmodo-based Hosanna Moda system bustier to improve learning independence and learning achievement

This study aims to: 1.) Develop an Edmodo-based e-module for making Hosanna Moda system bustier to increase independence and achievement in learning custom-made clothing; 2.) Describe the feasibility of the developed e-module; and 3.) Reveal the effectiveness of the developed e-module in increasing student learning independence and learning achievement. This research is development research, which was carried out on grade XII students of Fashion, State Vocational High School 3 Magelang. The product trial consisted of a validity test by three experts, a small-scale trial involving 12 students, a large-scale trial involving 24 students, and an effectiveness test involving one control and one experimental group each consisting of 36 students. The data were collected using questionnaires and tests as well as through interviews. The data analysis used is a quantitative description and the effectiveness testing using the t-test. The results of the study are as follows: 1.) The developed e-module contains learning materials about making the Hosanna Moda system bustier illustrated with relevant pictures to make it easier for students to understand it when studying independently; 2.) The product has been declared feasible by a material expert, media expert, and linguist. In the operational trial, teacher responses are with an average score of 5.00 (very good category and suitable for use), and student responses are with an average score of 4.59 (very good category and suitable for use); and 3.) The results of the effectiveness testing on student learning independence is at the significance level of 0.000 (0.050) indicating that the learning independence of experimental group students was higher than that of the control group, while the results of the effectiveness test on student learning achievement are at the significance level of 0.001 (0.050) indicating an average value. The posttest average score of the experimental group was much higher than that of the control group. Thus, it can be concluded that the developed e-module is effective in increasing student learning independence and learning achievement.
PEMBELAJARAN JARAK JAUH (PJJ) MELALUI APLIKASI GOOGLE CLASSROOM DI KELAS VII.2 SMP NEGERI 3 LUBUK LINGGAU

This study aims: to determine whether distance learning (PJJ) through the google classroom application can improve mathematics learning outcomes. In class VII.2 of SMP Negeri 3 Lubuklinggau, the research method used a questionnaire to collect observational data on the learning process in the form of teacher activities and activities. students at each stage of learning. For more details in obtaining these data, researchers used test techniques, observation techniques, and documentation techniques. In the first cycle, it can be seen that the average test (x1) is 75.31, 23 students complete the study, with the percentage of learning completeness is 71.88. In cycle II, it can be seen that the average test (x2) is 80.75, students who complete 28 people, with the percentage of learning completeness 87.50. The mean (x1) and (x2) increased by 5.44. The percentage of completeness learning (x1) and (x2) was 15.62%. So based on the results of the study it can be concluded that the use of the google classroom application can improve learning outcomes.
Investigating Users' Web Search Behaviour for Software Engineering and Maintenance Tasks

The Internet plays a key role in accomplishing many tasks. For many suchtasks, web search is integral in finding relevant information. Similar to otherdomains, web search is also heavily used in software engineering (SE) to helpwith various SE specific tasks such as debugging, finding documentation,installation, etc. In this paper, we present the first large scale study on howweb search is used in software engineering and maintenance. We analyze thequery logs from a major commercial web search engine. Being able todisambiguate software engineering queries from other queries is important forunderstanding the usage of web search in software engineering. To this end, webuild a machine learning based classifier to identify software engineeringqueries. We then analyze 1 million web search sessions to understand howsoftware engineering related queries and sessions differ from other queries andsessions. Subsequently, we define the taxonomy of intents behind the usage ofweb search by software engineers. This allows us to develop a betterunderstanding of the various contexts in which web search is used in softwareengineering and maintenance. We then analyse 6 million SE queries to understandthe distribution of intents across various web search metrics and other trendcharacteristics. Our results show that web search is heavily used for varioussoftware engineering and maintenance tasks. Finally, we discuss theimplications of this work on improving search engine support for softwareengineering queries by providing contextual assistance and we elaborate on howwe can gain insights into common issues faced by developers which can beleveraged to improve and maintain different software technologies.
Mobile Forensic on WhatsAppServices using National Institute of Standards and Technology Method

WhatsApp is a free chat application that allows users to exchange text messages, photos, videos, documents and can share the latest location available on Android and iOS versions built by Brian Acton and Jan Koum.One of the shortcomings of WhatsApp is that it can be used as a medium for committing crimes, such as hate speech.This study uses the National Institute of Standards and Technology (NIST) method, with the stages of collection, examination, analysis, and reporting with several applications that serve as tools to find digital evidence such as MOBILedit Forensic, WhatsApp Viewer, and DB Browser For SQLite.This study uses two smartphones that are evident with different conditions, namely a smartphone that has been in a root condition and a smartphone that is not in a root condition.Test results or looking for evidence using MOBILedit Forensic, WhatsApp Viewer, and DB Browser for SQLite on a smartphone in a root condition managed to find the data you were looking for.The data is in the form of conversational texts containing hate speech content.While the process of searching for evidence using MOBILedit Forensic on smartphones that are not in a data root condition, only images, videos, and audio were found, meaning that they did not find hate speech content in the form of conversational texts.Meanwhile, the process of searching for digital evidence using WhatsApp Viewer and DB Browser for SQLite on a smartphone that is not rooted does not find any data.
Graph Neural Networks with Local Graph Parameters

Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between $k$-tuples of vertices. The distinguishing power of these higher-order'' GNNs is known to be bounded by the $k$-dimensional Weisfeiler-Leman (WL) test, yet their $\mathcal O(n^k)$ memory requirements limit their applicability. Other proposals infuse GNNs with local higher-order graph structural information from the start, hereby inheriting the desirable $\mathcal O(n)$ memory requirement from GNNs at the cost of a one-time, possibly non-linear, preprocessing step. We propose local graph parameter enabled GNNs as a framework for studying the latter kind of approaches and precisely characterize their distinguishing power, in terms of a variant of the WL test, and in terms of the graph structural properties that they can take into account. Local graph parameters can be added to any GNN architecture, and are cheap to compute. In terms of expressive power, our proposal lies in the middle of GNNs and their higher-order counterparts. Further, we propose several techniques to aide in choosing the right local graph parameters. Our results connect GNNs with deep results in finite model theory and finite variable logics. Our experimental evaluation shows that adding local graph parameters often has a positive effect for a variety of GNNs, datasets and graph learning tasks.
Comparison of Artificial Intelligence and Human Motivation

According to Maslow's hierarchy of needs, humans act according to their needs. Humans also act with an interest in the internal process itself, rather than solely based on external rewards. Artificial intelligence, on the other hand, is trained through a large amount of data and neural network learning. Unlike humans who are greatly affected by emotions and various circumstances, AI is neutral and objective. With no clear limitations and vast potential for development. AI has been receiving a lot of attention. Many futurists say AI will replace humans in areas that we, in the past, couldn’t imagine machines doing the work. However, there are still areas where AI cannot replace humans. Metacognition to judge one's thoughts and creativity, the power to come up with new ideas, still remains in human domains that artificial intelligence has not been able to enter. This paper explores the reinforcement of humans and AI and explores the original human realm. Furthermore, understanding the original human domain can suggest developing human competitiveness.
Transfer-based Enrichment of a Hungarian Named Entity Dataset

In this paper, we present a major update to the first Hungarian named entity dataset, the Szeged NER corpus.We used zero-shot crosslingual transfer to initialize the enrichment of entity types annotated in the corpus using three neural NER models: two of them based on the English OntoNotes corpus and one based on the Czech Named Entity Corpus fine-tuned from multilingual neural language models.The output of the models was automatically merged with the original NER annotation, and automatically and manually corrected and further enriched with additional annotation, like qualifiers for various entity types.We present the evaluation of the zero-shot performance of the two OntoNotes-based models and a transformer-based new NER model trained on the training part of the final corpus.We release the corpus and the trained model.
Overcoming the challenges in morphological annotation of Turkish in universal dependencies framework

This paper presents several challenges faced when annotating Turkish treebanks in accordance with the Universal Dependencies (UD) guidelines and proposes solutions to address them.Most of these challenges stem from the lack of adequate support in the UD framework to accurately represent null morphemes and complex derivations, which results in significant loss of information for Turkish.This loss negatively impacts the tools that are developed based on these treebanks.We raised and discussed these issues within the community on the official UD portal.This paper presents these issues and our proposals to more accurately represent morphosyntactic information for Turkish while adhering to guidelines of UD.This work aims to contribute to the representation of Turkish and other agglutinative languages in UD-based treebanks, which in turn aids to develop more accurately annotated datasets for such languages.
Towards a Methodology Supporting Semiautomatic Annotation of HeadMovements in Video-recorded Conversations

We present a method to support the annotation of head movements in video-recorded conversations.Head movement segments from annotated multimodal data are used to train a model to detect head movements in unseen data.The resulting predicted movement sequences are uploaded to the ANVIL tool for postannotation editing.The automatically identified head movements and the original annotations are compared to assess the overlap between the two.This analysis showed that movement onsets were more easily detected than offsets, and pointed at a number of patterns in the mismatches between original annotations and model predictions that could be dealt with in general terms in post-annotation guidelines.
Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification

We present a new form of ensemble method-Devil's Advocate, which uses a deliberately dissenting model to force other submodels within the ensemble to better collaborate.Our method consists of two different training settings: one follows the conventional training process (Norm), and the other is trained by artificially generated labels (DevAdv).After training the models, Norm models are fine-tuned through an additional loss function, which uses the DevAdv model as a constraint.In making a final decision, the proposed ensemble model sums the scores of Norm models and then subtracts the score of the De-vAdv model.The DevAdv model improves the overall performance of the other models within the ensemble.In addition to our ensemble framework being based on psychological background, it also shows comparable or improved performance on 5 text classification tasks when compared to conventional ensemble methods.
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
Аналіз складових моделі розвитку рухових якостей студенток закладів вищої освіти в умовах професійної підготовки на акмеологічних засадах

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
ANALISIS KAUSALITAS KRIMINALITAS, PENDIDIKAN DAN KEMISKINAN DI INDONESIA

This study aims to examine the causality of crime, education and poverty in Indonesia. The data used is panel data during the period 2013-2017, with the technique of collecting documentation data and library studies obtained from relevant institutions and agencies. The variables used are Crime (Y1), Education (Y2), Poverty (Y3). The research methods used are: Granger Causality Test Panel and Vector Autoregression (PVAR) Panel. This study found that crime does not have a causal relationship to education, and vice versa. Crime does not have a causal relationship to poverty and vice versa. And poverty has a one-way relationship to education. The results of the PVAR found that crime in a given year period was significantly influenced by crime in the previous period. Likewise, it was also with education and poverty.
Analysis of Topics of Interdisciplinary Research in the Field of Library Science and Information Science from the Perspective of Altmetrics

On the basis of identifying the interdisciplinary research topics in the field of Library and Information Science, this paper constructed Altmetrics indicator systems and makes SOFM neural network clustering analysis on the comprehensive influence of the research topics, in order to provide reference for the interdisciplinary crossover and integration of Library and Information discipline in China under the background of "New Liberal Arts".The study found that the 20 interdisciplinary research topics could be divided into four types according to their influence composition characteristics: comprehensive hotspots, online social hotspots, academic hotspots, potential hotspots.Interdisciplinary hot topics were important for the intersection and integration of library and information field and other disciplines, and high impact research topics deserved close attention from Chinese scholars.
Workshop menghindari plagiasi dengan teknik parafrase pada penulisan karya ilmiah mahasiswa tingkat akhir 2021

Scientific paper often becomes the toughest obstacle faced by students. This community service article was written based on a workshop with the same theme which aims to avoid plagiarism on writing scientific papers for final-year students in Salatiga with paraphrasing techniques. The workshop was held online via Zoom application and joined by 20 eight-semester students in Salatiga 2021 who had difficulties in writing their final assignments. The activities were: material presentation on paraphrasing techniques, question and answer session, paraphrasing techniques practice directly from scientific text sources according to the student's scientific field, and direct feedback/evaluation to students. At the end of the activity, the presenters as well as the authors distributed a link to check students’ understanding questionnaires about paraphrasing techniques in general. From these activities, it can be concluded that the paraphrasing technique is very important for students and is needed to reduce the percentage of plagiarism check results. In addition, from the results of the questionnaire, overall, all participants understood and could practice well the paraphrase technique material from the Workshop.