Reviewerly/exHarmony huggingface dataset:
MEASURING PASSENGER SATISFACTION LEVEL USING GAP SCORE ANALYSIS AND IMPORTANCE PERFORMANCE ANALYSIS AT TRAIN STATION

&lt;p&gt;&lt;em&gt;T&lt;/em&gt;&lt;em&gt;he government continues to promote public transportation so that people living in Jakarta can switch from private transportation to public transportation to reduce congestion.&lt;/em&gt;&lt;em&gt; Rail transportation is one of the widely used modes of transportation by people when they want to travel. However, at the beginning of 2020, there was a decline in passengers and there were several regulations for traveling, this was useful to suppress the level of spread of the Coronavirus Disease 2019 (COVID-19). Regulations and conditions for passengers who will travel by train must comply with several things, such as being obliged to follow the instructions and rules that already exist at the train station, conducting a rapid test, and having proof that the results must be negative. This research is conducted using the service quality method and Importance-Performance Analysis (IPA). The level of service provided by the station still must be improved to maintain the satisfaction of the passengers, because the gap scores still have a negative value. Several things cause the passengers to still not feel satisfied with some services. Eight attributes have a gap value greater than the average gap obtained. The company should continue to pay attention to the comfort and satisfaction of passengers when using train services to travel somewhere. Especially at the service level related to rapid tests, more attention can be given because there are so many passengers who will carry out rapid tests in the station area.&lt;/em&gt;&lt;/p&gt;
Can the virtual reality intervention VR FestLab improve Danish studentś alcohol competences?

Abstract Background VR FestLab is a newly developed simulated “party” where the player is being offered alcoholic beverages while steering their own party experience in virtual reality. We evaluated the efficacy of VR FestLab on drinking refusal self-efficacy of Danish male and female 15-18-year-old students and tested user satisfaction. Secondary outcomes were drug refusal skills, knowledge/awareness, communication skills, social support willingness, susceptibility to peer pressures, and outcome expectations. Methods The intervention consisted of 15 minutes of gameplay and 30 minutes of group/in class discussion. The intervention group played VR FestLab where the control group played the VR game “Oculus Quest - First Steps” (no educational content). In a cluster-randomised controlled trial schools were randomly assigned to the intervention/control group. 13 Danish schools were recruited containing 181 students in intervention/191 in control groups. Students completed a questionnaire before and immediately after the intervention. Data was analysed using mixed linear regression models. Results 50% of students found VR FestLab to be realistic, 57% would like to explore it further, and 43% would recommend it to friends. We found a small, but non-significant effect on drinking refusal self-efficacy favoring the intervention. No differences between the intervention and control group were observed for the secondary outcomes. Conclusions VR FestLab should be used with higher frequency than in this trial and in combination with other evidence-based alcohol prevention interventions adolescents. Further research is needed to improve the effectiveness of VR FestLab. Key messages • VR FestLab could be a valuable contribution to school-based alcohol prevention education but cannot stand alone. • Future research should focus on the dose-effect relationship of the novel tool.
ANALYSIS OF ENGLISH CLAUSE PATTERNS: (A Qualitative Analytical on Clause Pattern in Novel)

This research entitled Analysis of English Clause Patterns: A Qualitative Analytical on Clause Pattern in Novel. The aim of this research is to give an explanation in syntactic analysis of the three clause patterns and to give an explanation of each of them in ”Fox Evil”novel by Minette Walters. Clauses can be categorized by the verb used in it. There are two verbs, transitive and intransitive verb. Transitive verb devided into two, monotransitive verb is verb has only one object that called as direct object and ditransitive verb has two objects namely direct object and indirect object. The method that the researcher used in this research is descriptive qualitative as its method to analyze and describe entire data. The data are taken from “Fox Evil” novel by Minette Walters. After getting the data, the researcher analyzed it syntactically. In addition, it is able to be concluded this research those sentence that using transitive verb can analyze based on syntactic unit perspective. Based on the data findings, the writer found that there are 30 data, those are analyzed syntactically based on the clause patterns. The entire three clause patterns were found in the novel fox evil, with details: clause pattern S + V + O as much as 10 data (33,3%), clause pattern S + V + C as much as 10 data (33,3%), clause pattern S + V + AD as much as 10 data (33,3%).
THE USE OF DICTOGLOSS TO IMPROVE STUDENTS’ WRITING IN MUHAMMADIYAH PLUS SECONDARY SCHOOL BATAM

Teaching writing in secondary school is found as another level of skill to be mastered (Abdulwahed and Ismail, 2011; Pertiwi and Drajati (2018); Ardiansyah, (2020)). Research about teaching writing at this junior high school level often got less attention which happened in Muhammadiyah Plus Secondary School Batam. However, students at this level are supposed to be able to master the writing skill as well as learn other skills in English. In implementing that, thus, a teaching method is needed to be chosen. In this study, the researcher will examine the use of Dictogloss as a technique to improve secondary students’ writing skills. Dictogloss is going to use as a treatment to help students to develop their grammar, vocabulary, spelling, and punctuation as found as the mostly error made by them. This learning which will integrate dictation given by the researcher and peer-assessment as a technique to analyse is expected to be beneficial. Hence, the aim of this study is to analyse the use of Dictogloss to improve students writing skills. The participants are the students from grade 9A, 26 students in total, in Muhammadiyah Plus Secondary School Batam. The class subject is going to be The English Cambridge exposing about Procedure Text. In order to apply the research, a Classroom Action Research (CAR) is selected as the research method to use. Keywords: dictogloss, writing skill, teaching writing, secondary students
Calculating the area of white spots on the lungs of patients with COVID-19 using the Sauvola thresholding method

&lt;span lang="EN-US"&gt;COVID-19 is a pandemic that has occurred in the world since 2019. Researchers have carried out various ways in dealing with this disease, starting from the screening stage to the stage of treatment and therapy for COVID-19 patients. As the gateway to the COVID-19 problem, screening has an essential role in a diagnosis that leads to appropriate treatment. In this paper, we will focus on the screening stage using digital image processing techniques, namely in calculating the area of white spots in the lungs of COVID-19 patients. The white patches are an early indication of how badly COVID-19 is attacking the patient. We use X-Ray Thorax image objects as research data in this paper. Although the current experimental results show that this method has a successful performance of 71.11%, it is pretty promising for further development due to its simplicity.&lt;/span&gt;
Target region preserving smoothing for selective image segmentation

Abstract Selective image segmentation is an important and challenging task, which aims to segment a subset of target objects or regions of interests in an image. In this paper, we propose a two-stage selective segmentation method. In the first stage, we propose a new smoothing model, which aims to reduce the influence of noise or cluttered background on segmentation. The model consists of a regular term and a fidelity term. First, we define a plug-and-play regular term by incorporating the trained fractional optimal control network. Then we introduce the fidelity term a weight dependent on the distance function of a set of marker points in the target region. In addition, the proposed model has a closed-form solution, which is easy to implement. In the second stage, the smooth image obtained in the first stage is divided into background region and target region with a simple threshold value. Extensive experiments show that our smoothing model can greatly facilitate the second phase, and our method performs better than some existing related methods in terms of either visual assessment or quantitative evaluation.
Remote education system for physics labs based on Arduino and javaScript

Abstract In 2020, a pandemic began, which stopped many of the academic activities around the world; in our university the emerging distance education program PEER by its acronym in Spanish, was implemented which allowed continuity to academic activities of the students. Some theoretical courses could be carried out through video meetings without any problem. But for the labs classes that always have required practices, there was no adequate solution, even if these laboratories are essential for engineering careers. Therefore, we took on the task of design and build an educational system using Arduino development platform and light emission and detection components, in which the most common practices carried out in the university facilities could be implemented but now from anywhere without the need for a large investment of money in specialized measurement equipment.
Who is following me? Public attitude towards government tracing apps in the covid Era in Israel

Abstract Background In the battle to decrease coronavirus infection and mortality, Israel has employed emergency tools, e.g., tracking civilians’ locations via their cellphones or activating the HaMagen app that identifies when a person is near someone who has been diagnosed with the virus. While the aim of these tools is to ensure the public’s health, they could harm human rights. Objective To examine the Israeli public’s attitudes towards enhancing public health during the pandemic while preserving privacy, by examining the relationship between trust in the healthcare system, threat perceptions, cellphone tracking, and HaMagen App. Methods Surveys (distributed by iPanel) was completed by 741 adults, aged ≥18. Results About half the respondents (47.1%) perceived cellphone tracking as harmful to privacy, yet one-quarter (24.4%) reported that this increases their sense of security. About half (48.4%) agreed/greatly agreed with the item whereby the government uses the gathered data for non-coronavirus purposes. Jewish respondents had more positive attitudes towards government tracking than Arab ones, yet the latter reported higher downloading of HaMagen. The findings indicate that threat perceptions and positive attitudes towards cellphone tracking were related to greater chances of downloading the app. Moreover, attitudes towards such tracking were mediated by the relationship between trust levels/threat perceptions and downloading the app, whereby the former was association with more positive attitudes towards cellphone tracking, which in turn was related to greater app downloading. Conclusions and Recommendations Trust plays a central role in people’s willingness to forgo their privacy for the good of public health. To enhance trust, messages must be suited to a range of communities, presented in a suitable language by local professionals. Key messages • Trust plays a central role in people’s willingness to forgo their privacy for the good of public health. • Concern for public health must include ethical considerations.
Implementation of “COVID-19 case investigation and contact tracing” training program in Armenia

Abstract Background Comprehensive contact tracing and case identification (CICT) is key to preventing the SARS-COV-2 transmission. During the COVID-19 waves in Armenia, the effectiveness of CICT practices was suboptimal due to weak capacity of evidence-based CICT. The American University of Armenia and the University of California, Los Angeles offered a continuous education program for the public health workforce in Armenia which is relevant for any country aiming to improve its CICT capacities. Objectives We developed and implemented a fully remote yet synchronous ‘COVID-19 case investigation and contact tracing” training program through didactic lectures, demonstrations of CICT interviews, small-group skill labs, and opinion polls. It covered the basic principles of public health and epidemiology, the main methods of CICT in relation to COVID-19, and health coaching techniques. We evaluated the knowledge improvement through a pre-experimental evaluation design at baseline and follow-up. The training sessions were held from November 2020 to June 2021. The participants received 10 CME credits upon completion of the training course. Results The online modality allowed to reach professionals across the country, though affected the participation in the evaluation surveys. Overall, 93 professionals participated in the training program, yet only 57 returned completed surveys. The paired analysis showed an increase in the mean knowledge scores (0-24) at baseline and follow-up (14.5 vs 15.82 (p = 0.0083). A more notable increase was detected on questions that measured knowledge of health coaching techniques (0-6) (2.76 vs 3.44 (p = 0.0052)). Conclusions New knowledge and skills penetrated into daily CICT practices across Armenia. This was a good example of a quick mobilization of local and international expertise to assist the national efforts in responding to a public health emergency by utilizing evidence-based approaches and lessons learned from the past infectious diseases’ outbreaks. Key messages • Our training program strengthened the human resource capacities of Armenia’s existing public health system and assisted Armenia’s COVID-19 response efforts. • The model of our training program can be effectively adapted and extrapolated for various countries and settings which aim to improve response capacities to other threats of public health importance.
Multi-labeling with topic models for searching security information

Abstract Security information such as threat information and vulnerability information are utilized to analyze cyberattacks. If specific keywords such as the name of malware related to the event to be analyzed are known in advance, it is possible to obtain information using typical search engines. However, when a security operator cannot recall appropriate keywords related to the event to be analyzed, or when a commonly recognized identifier does not exist, a general search engine cannot be expected to produce useful results. In this paper, we propose a method using topic models and outlier detection to generate multi-labels for search, with the goal of constructing a search engine that can present relevant security information even in such situations. In addition, this paper discusses the application of the proposed method to 2386 security reports issued from 2017 to 2019 to demonstrate that the labeling can be focused on specific topics.
Open research case study - Computer Science

Openness and transparency constitute a foundational principle for research integrity, as set out in the UK Concordat to Support Research Integrity. Openness can promote rigour, constructive scrutiny, accountability and can enable others to build on research. However, it can also bring challenges. Critically, what openness and transparency can and should mean varies across disciplines and fields of study. This is one of a series of case studies in a wide range of disciplines that illustrate these differences. The series is intended to enable researchers to see similarities and differences between fields, and to inform those supporting open research through, for example, training, policies or incentives. It is based on a single interview with a researcher, and is therefore illustrative rather than representative.
SentiLexBR: An Automatic Methodology of Building Sentiment Lexicons for the Portuguese Language

User reviews are readily available on the Web and widely used for sentiment analysis tasks. Sentiment lexicons plays an important role in sentiment analysis, where each sentiment word is given a sentiment label (positive or negative) or score (1 or -1). However, a sentiment lexicon may express different sentiment polarity according different domain. In addition, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical corpora. In this paper, we present an effective methodology, called SentiLexBR, using probabilities of the Bayes’ Theorem for building a set of sentiment lexicons. An unsupervised algorithm is proposed to automatically identify sentiment lexicons with their polarities for the Portuguese language. Experimental results on user reviews datasets in 12 different domains indicate the effectiveness of our methodology in domain-specific sentiment lexicon generation for Portuguese. In addition, the sentiment lexicon produced by SentiLexBR also significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.
Scientific Collaboration Network Views: A Brazilian Computer Science Graduate Programs Case

Scientific collaboration networks can present different views of researchers’ interactions. This work presents SCI-synergy, an online navigable artifact aiming to promote mechanisms and views of scientific collaboration networks. The artifact focuses on the researchers’ interaction in the co-authorship of publications considering intra- and interprogram relationships. SCI-synergy is developed upon the design science research paradigm using scientific publication data available on the large Digital Bibliography &amp; Library Project (DBLP) repository. Official data from the Sucupira repository of six Brazilian graduate program members including Federal University of Minas Gerais (UFMG), State University of São Paulo (USP), Federal University of Rio Grande do Norte (UFRN), Federal University of Amazonas (UFAM), University of Brasília (UnB), and University of Vale do Rio dos Sinos (UNISINOS) is used. Data from these graduate programs illustrate the artifact usage regarding the scientific collaboration network of each program, how each researcher cooperates, and what relationship patterns exist in intra- and inter-programs views. We advocate that, even though it is necessary to consider data from each program’s history and current contextualization regarding politics, economics, and administration, the collaboration network views provided by SCI-synergy might help to understand collaboration network patterns.
Sentimental Analysis using Bert Algorithm over LSTM

Sentiment analysis also referred to as opinion mining, is an approach to natural language processing (NLP) to find out whether the meaning of the given data is positive or negative. There has been a lot of research done in this sector to increase the accuracy of sentiment analysis systems, ranging from basic linear models to complicated neural network models. Other models or algorithms were checked previously but these took longer time for operations and had low accuracy scores overall. So, in this paper, the BERT model, which has been pre-trained on a huge corpus, is offered to address concerns with sentiment analysis systems. Further fine-tuning improves results according to the use cases. The BERT model gives high accuracy, and efficient performance with good predictions as compared to other models in experimental testing. We are particularly interested in performing sentiment mining techniques over transcribed audio recordings to detect the speakers' sentiment using a Google specialised algorithm BERT (Bidirectional Encoder Representations from Transformers), which is a faster and more accurate algorithm for text fields because it can also consider the context of the text. Our study was initially driven by call centre use cases, but it can potentially have applications in other domains such as security and other areas. So in this research project work we will be giving input as an audio file from which the text will be generated, and out of which we will be finding the sentiment of that audio stream by using the BERT model.
Generic Structure Of Thesis Abstracts Written By Undergraduate Students

Due to a large number of academic information available throughout the world, research abstracts have become extremely important. Abstracts are used by readers to access an article, by journals to pick contributions, by conferences to approve or reject papers (Lores, 2004 as cited in Al-Khasawneh, 2017). Therefore, in this study, the writer would like to analyze the generic structure of a thesis abstract by using Swales &amp; Feak’s (2004) model, namely (1) background, (2) aim, (3) method, (4) results, and (5) conclusion. In order to analyze the genre or generic structure of thesis abstracts, this study uses a qualitative case study approach. Ten (10) English thesis abstracts written by undergraduate students from the English education study program are collected using purposeful random sampling. The researcher sorts the thesis abstracts from the last two years and the theses which have used the quantitative method as their methodology. Findings show that the purpose move (M2), the method move (M3), and the result/finding move (M4) were the most frequent moves that occurred in the abstracts written by undergraduate students enrolled in the English education study program. However, the introduction move (M1) and the conclusion/discussion move (M5) occur less frequently. It could be concluded that the majority of the thesis abstract follows Swales &amp; Feak’s (2004) five-move pattern. While in some thesis abstracts, the move “introduction” and “conclusion” are not included.
The Influence of the Use of Google Meet with the Assistance of Google Classroom on Interest and Learning Outcomes of Mathematics in Class V Students of Ipeka Christian Elementary School Makassar

This research aimed to analyze the impact of Google Classroom assisted by Google Meet use on the interests and learning outcomes of students' mathematics Class V Kristen Elementary School. This type of research was quantitative research with quasi-experiment. The research design used was a non-equivalent control group design. The population in this study were the students of class V of Kristen Elementary School IPEKA Makassar, with a total of 60 students divided into two classes, VA and VB. Va class was the experimental class, and VB class was the control class. The technique of data collection was using tests and learning interests. Data analysis techniques in research used descriptive and inferential statistical analysis. Based on the data analysis, the average learning interest of experimental class students was 73.33, and control classes were 59.33. In addition, classically the interest in learning experimental classes reached 80%. The average results of experimental math learning classes were 93 while the average results of learning math control classes were 69.67. In addition, the completion of classical learning experimental classes reached 100%. The N-Gain Test obtained a coefficient of 0.841 and was categorized as high. Test the hypothesis using an independent sample t-test. In statistical analysis, learning interests were obtained sig value. (2-tailed) = 0.00 &lt; 0.05. In the analysis of post-test results of learning results obtained Sig values. (2-tailed) = 0.032 &lt; 0.05. From the data, it can be concluded that the use of Google Meet assisted by Google Classroom affected the interests and results of learning mathematics students of class V Kristen Elementary School IPEKA Makassar.