Reviewerly/exHarmony huggingface dataset:
Generating Counterfactual Instances for Explainable Class-Imbalance Learning

Existing class imbalance learning paradigms focus on lifting the importance of minority instance, aiming to improve the model in terms of certain evaluation metrics (e.g., AUC and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F_{1}$</tex-math></inline-formula> -measure). One drawback of these methods is that they lack enough transparency, hence, cannot be fully trusted in vital domains. To this end, this paper deal with the class imbalance learning task with counterfactual instances. Given an instance and a classifier, a counterfactual is a fake instance which, while having smallest distance to the original instance, is classified as a different class by the classifier. Therefore, the most important features for a classifier can be identified by inspecting the difference between an instance and its counterfactual. To utilize counterfactuals, a novel Explainable Generative Adversarial Network (EXGAN) is proposed. EXGAN has a unique “two generators <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vs.</i> multiple discriminators” architecture where the generators are used to generate effective counterfactuals and discriminators are trained for the class imbalance learning task. In addition to the architecture, an innovative ensemble loss function ensuring each discriminator complementing each other is designed to overcome the class imbalance issue. Extensive experiments prove that the counterfactuals generated by EXGAN can be used to produce effective local explanation and provide significant better class imbalance learning ability than existing competitors.
Keynote Game Theory and Cybersecurity: Multiagent Security Issues, Mathematical Modelling and Computer Science Applications

Cybersecurity is a multiagent system where intelligent agents interact, formulate strategies, fight, cooperate, coordinate, design systems, and plan actions to achieve their goals of cybersecurity or hacking and malicious damages. Game theory analyses and formulates strategies and designs rules or mechanisms for this cybersecurity multiagent system on the basis of artificial intelligence. For specifying, characterising and modelling and designing this intelligent multiagent system, mathematical game theory models of different forms can be developed, such as static, dynamic, evolutionary, differential and stochastic game theory models. Different algorithms such as Nash equilibrium, joint optimisation, evolutionary algorithms, neural networks, genetic algorithms, and other machine learning algorithms can be applied to different game theory models for analysing, solving, and computing these cybersecurity models. Findings from these models are used to formulate strategies, cooperate, coordinate, design systems, and plan actions by different intelligent agents and authorities in cybersecurity. Game theory application in cybersecurity is an important area in computer science for doing highly useful academic and practical cybersecurity activities and for academics and practitioners to build their careers. Therefore, it is necessary to prioritise this area of game theory and cybersecurity in computer science for research and development.
Advances of Cyber Security in the Healthcare Domain for Analyzing Data

Analyzing healthcare data is an important part of improving patient outcomes and driving healthcare innovation. With the increasing digitization of healthcare records and the growing use of electronic health records (EHRs), there is a wealth of data available that can be used to inform clinical decision-making. Analyzing healthcare data comes with its own unique set of challenges. Healthcare data can be complex and voluminous, and it often exists in disparate systems and formats. Cybersecurity is becoming increasingly important in the healthcare domain, as the industry is rapidly digitizing and relying more on electronic data management systems. This chapter analyzed the current state of cybersecurity in the healthcare domain, discussing the common types of cyber threats faced by healthcare organizations and the potential impact of these threats on patient care. The goal of this chapter is to provide an overview of healthcare data analysis and to highlight the ways in which security feature can be used to drive improvements in patient care and healthcare outcomes.
Layout Analysis of Document Images

In order to extract information of interest from document images, their content must be recognized. To that end, layout analysis is an important step. In layout analysis one is concerned in finding page components such as text blocks, tables, formulas, diagrams, and determining their logical role. That could enable building of efficient representation of document content, with application possibilities such as determining the right reading order, establishing relationships between page components, or improving indexing in general. In this work, we revisit the layout analysis problem reviewing how it is being impacted by advances in the field of machine learning. Then, we conclude pointing some future works.
The Effect of Media Exposure, Environmental Performance, and ISO 14001 Certification on Carbon Emission Disclosure

Climate change is increasingly having a negative impact on people’s lives as well as the environment. Various diseases related to ambient air pollution are increasingly becoming the cause of death. Natural disasters also occur frequently, causing huge losses. Greenhouse gas emissions, often called carbon emissions, are one of the biggest causes of climate change. This research aims to provide empirical evidence regarding the effect of media exposure, environmental performance, and ISO 14001 certification on carbon emission disclosure. Research data were obtained from the Indonesia Stock Exchange website, the PROPER Secretariat website of the Ministry of Environment and Forestry in the Republic of Indonesia, and corporate media covering nonfinancial companies (energy, industry, basic materials, transportation and logistics). The research sample selection used purposive sampling, resulting in 95 unbalanced data for three years (2019-2021). This research uses panel data regression analysis to examine the effect of media exposure, environmental performance, and ISO 14001 certification on carbon emission disclosure. The results showed that media exposure and environmental performance positively affect carbon emission disclosure. Meanwhile, ISO 14001 certification has no effect on carbon emission disclosure.
A Study on Gender and Sexuality in Chemistry Teaching

This article presents the results of a state-of-the-art research that aimed to investigate the theme of gender and sexuality in the teaching of chemistry.The mapping of research was carried out from 2010 to 2021 in the Brazilian Digital Library of Theses and Dissertations of Capes (Coordination for Higher Education Personnel Improvement) and in the Portal of Periodicals of Capes.In the National Meeting of Chemistry Teaching and National Meeting of Research in Science Education, the period was from 2010 to 2020.43 works were identified that involved the theme, however only 8 researches involved chemical contents.A large part of the inventoried researches encouraged the participation of women in the scientific career.In this sense, it is inferred that the theme gender and its nature involving chemical concepts are still incipient in chemistry classes.
Diabetes Diagnostic Expert System using Website-Based Forward Chaining Method

Diabetes is a chronic disease. The World Health Organization predicts that Indonesia's number of diabetic patients will continue to increase significantly to 16.7 million in 2045. As early prevention, early diagnosis is needed to anticipate more severe diabetes. This study aims to build an expert system for detecting diabetes using a web-based forward chaining method. The expert system is built by collecting indications from experts by collecting facts using the forward chaining method. Furthermore, judging by the unhealthy lifestyle of many people who consult with hospitals or health workers. From the results obtained, the system can work well based on knowledge from experts
Prediction of Covid-19 Cases in Central Java using the Autoregressive (AR) Method

Since the beginning of the Covid-19 case in Indonesia in March 2020, more than 6 million confirmed cases had been confirmed. The rapid development of this case can be accessed through the covid19.go.id page. In Central Java province, confirmed cases as of July 6, 2022, reached 628,393 people, with the number of recovered patients reaching 594,783 people and the number of patients dying as many as 33,215 people. With this data, a prediction is needed to help the government anticipate an increase in Covid-19 cases in Central Java Province. This study aims to create a forecasting model using the Autoregressive (AR) method by optimizing the function parameters. Then Mean Squared Error (MSE) to analyze the results of forecasting data errors. The results are the best parameter functions on AR (30) with the smallest MSE. Furthermore, predictions are made from July 1 to August 30, 2022, showing an increase in cases
The impact of the COVID-19 pandemic on education: the online economic mathematics course

The COVID-19 pandemic had a considerable impact on education around the world, interrupting the continuity of the educational process, switching to online or mixed studies. Both teachers and students had to adapt to a new study format in a very short time. The paper aims to analyse the advantages and disadvantages of online studies and to analyse the online teaching of the mathematics for economists course based on the results of the questionnaire in which first year ASEM students participated.
IMPLEMENTATION OF STRING SIMILARITY ALGORITHM IN PUBLIC COMPLAINT APPLICATIONS TO MINIMIZE SIMILAR COMPLAINTS

Presently, the complaining service in Kartasura local government relies on manual recording where people must come to the government office, write their complaints and submit them to the office staff. This situation causes inefficiency since people have to travel from their places to the local government office. Moreover, the manual recording makes the complaints cannot be managed properly since the same complaints can be submitted more than one time. Additionally, it also causes some confusion to the government staff since they need to carefully check whether it has been submitted previously and approve the complaints accordingly. To solve the issue, a web-based application complaint system is developed to reduce the number of the same complaints from the citizen as well as to help staff manage the complaints data. The Jaro-Winkler String Similarity algorithm is adopted to check the similarity of newly submitted complaints with existing complaints data. The algorithm detects similarities of newly submitted complaints by determining the level of string equality to the existing complaints. Experimental results using one month period of complaints data in December 2022 show that the application is able to detect the similarity between the newly submitted complaints to the existing complaints. As the value of similarity threshold is higher, the number of rejected complaints also increases. Meanwhile, the test results of the system using the System Usability Scale Score obtained an average value of 76.5, which means the system is included in the Acceptable category and can be used for daily activity.
DECODING SOCIO-COGNITIVE ELEMENTS IN INDONESIAN ONLINE NEWS: A SYSTEMIC FUNCTIONAL MULTIMODAL DISCOURSE ANALYSIS

This study explores the representation of socio-cognitive components in Indonesian political multimodal news text using the framework of Systemic Functional Multimodal Discourse Analysis (SF-MDA). With the increasing prominence of multimedia in contemporary news production, understanding how socio-cognitive aspects are conveyed through multiple modes becomes essential. Drawing on SF-MDA, which combines systemic functional linguistics and multimodal analysis, the study examines how Indonesian political news texts incorporate social and cognitive elements to shape meaning and influence audience perceptions. This study was undertaken qualitatively involving three online news published by Indonesia’s publishers as the data. The result reveals that the visual and verbal modes merged in the news tend to highlight the good image of the political figures. It means that the meanings of visual and verbal components of the news persuasively work together to create the good impressions among readers to the human actor in the news. As the implication, multimodal resources are strategically employed to shape public opinion and promote specific political ideologies. Ultimately, this research aims to contribute to the development of critical media literacy and foster a more nuanced understanding of the socio-political dynamics in contemporary Indonesia.
WHATSAPP USE ON READING COMPREHENSION: EXPLORING THE IMPACT AMONG EFL LEARNERS

The study aimed to explore the impact of WhatsApp use on reading comprehension among English as a Foreign Language (EFL) learners in Indonesia. Results from the questionnaire showed that a majority of the participants used WhatsApp for reading activities often or very often, with new articles, fiction or literature, and non-fiction books being the most common types of reading materials accessed. The majority of EFL learners spent 1-2 hours on WhatsApp for reading activities, and cited convenience and accessibility as the main reasons for using the platform. The results also showed a significant number of EFL learners believed that using WhatsApp had improved their reading comprehension skills, although some participants did not find it to be an effective tool. The most commonly cited advantage of using WhatsApp for reading activities was convenience and accessibility, followed by engagement with peers and opportunities for discussion and exchange of ideas. Meanwhile, the most commonly cited disadvantage was distraction from other conversation or notifications, limited access to a wide range of reading materials, and lack of interaction with the teacher or tutor. The results also indicated that the majority of EFL learners improved their reading comprehension skills over time when using WhatsApp for reading activities, with a focus on reading texts or materials, participating in group discussions, and completing reading comprehension exercises. The study results provide valuable insights into the impact of WhatsApp use on reading comprehension among EFL learners and could help identify areas for improvement in utilizing the platform for reading activities.
UTILIZING INDONESIA’S SUPERDIVERSITY AS A LEARNING SOURCE IN CREATIVE WRITING TEACHING FOR INDONESIA EFL STUDENTS

Research on English creative writing has been carried out by many researchers both in Indonesia and outside Indonesia by considering how important creative writing is for learning literature in general and learning English in particular. Likewise, research on Indonesia’s super-diversity. However, research on English creative writing using Indonesian superdiversity as a learning source is still very little. Therefore, this qualitative descriptive method research is conducted. This study aims to explore benefits of teaching of creative writing utilizing Indonesia’s superdiversity as a source of learning. To achieve the aim, a questionnaire and document of students' artifacts are employed as research instruments. 59 participants from Creative Writing Language Education: Poetry class are involved; they are convenient and purposive samples. The result showed that almost all participants are very interested in learning about and making creative patterned poetry utilizing Indonesia’s super-diversity because of its many benefits. Similar research can also be carried out in other subjects so that the values of tolerance and respect become a beautiful culture amid Indonesian people's lives.
THE ROLE OF TEACHERS IN OVERCOMING POST-PANDEMIC LEARNING LOSS IN ELEMENTARY SCHOOLS

This study aims to knowing the teacher role in planning learning to overcome post-pandemic learning loss in class 3 SD Negeri Gendol 1, knowing the role of the teaher in overcoming students reading skills due to post pandemic learning loss in class 3 SD Negeri Gendol1, knowing the teacher role as a motivator forpost-pandemic students in class 3 SD Negeri Gendol1.This study use a qualitative method, in which the researcher directly observes the teacher role as a motivator for students, especially in class 3. The data collection process is carried out using 3 techniques, observation, interviews, and documentation. In data analysis activities consist of 3 paths, data condensation, data presentation and drawing conclusions. After the researcher conducted the research, the researcher could conclude that learning had been carried out online, the use of learning applications during a pandemic, adjusted the curriculumused, the teacher prepare lesson plans, the teacher used interesting learning media, class 3 students had different reading abilities, the teacher carrying out reading habits for students, the teacher makes students to be more active in reading during the learning process,and provides library space and books, the teacher gives appreciation to students, as well as the occurrence of good communication between teacher and parents.
Minat Mahasiswa Menjadi dan Tidak Menjadi Akuntan Publik

The aim of this research is to find out the factors that differentiate the interests of students of the Bachelor of Accounting Study Program at Udayana University between those who wish and those who do not wish to engage in the public accounting profession based on Maslow's theory of motivation. The study used the census method with a sample of 262 students of the Udayana University Bachelor of Accounting Study Program who participated in the survey. Research data was collected using a questionnaire instrument based on Google Forms which was distributed via social media Line Group class 2015 to 2021. The data collected was then analyzed using discriminant analysis. The results showed that physiological needs and appreciation needs significantly differentiated students' interest in becoming and not becoming public accountants, while safety needs, social needs and self-actualization needs did not significantly differentiate students' interests in becoming and not becoming public accountants.&#x0D; Keywords: Student Interests; Public Accountants; Maslow's Motivation Theory; Discriminant Analysis
RAINFALL PREDICTION USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GEOGRAPHIC INFORMATION SYSTEM APPROACH

Rainfall is one indicator to determine the estimated adequacy of groundwater on agricultural land. The groundwater availability produced by rain can determine cropping patterns in an area. The availability of rainfall data depends on the accuracy of information on current climate conditions. This case causes the related parties to find difficulty determining the classification of cropping patterns in the future. Accurate rainfall prediction models are needed to overcome the problem of shifting rain patterns. Rainfall prediction models in determining cropping patterns are recommended by FAO, such as linear regression, which is still widely used today. This study aims to develop a new model of rainfall prediction by using the method SARIMA to determine cropping patterns to increase crop yields. Rainfall data was used from 2010 to 2020 from seven rainfall collection stations in Sleman Regency, and they are used as training data to predict future rainfall. The output of the data analysis is a prediction of rainfall in the range of January-April, which is predicted to be high, May-August, which is predicted to be low; and September-December, which is predicted to be moderate. In addition, based on the identified cropping patterns, recommendations can be given to farmers to set cropping schedules and strategies to increase the productivity of the farmland. The testing of accuracy forecasting used relative mean absolute error (RMAE) for 12 months. The results of the forecasting accuracy test for 12 months in Sleman Regency showed RMAE average of 1.46 was considered low, for it was still below 10%.
Mathematics Mobile Game Visual Asset Design

Mathematics is one of the compulsory subjects for students in Indonesia. Mathematics is one of the quality of learning subjects, especially math during the epidemic seoson, the learning materials are effective, interesting ang not boring for students. One way is to create educational games using mobile phones. Educational game is an Android based mobile game that makes it easier for users to use it anywhere and anytime than other platforms, same as a computer. Therefore, the problem posed in this research is How to generate mathematical mobile game image assets? Solution to this research problem is how to design visual assets with a natural theme. So create some icons, buttins and background design with forest design, wood design and another math related objects. It is then incorporated into the final design. End of the result from this research is visual game assets can be used to make games easy to operated and enjoyable, especially for elementary student in grades 3 and below.
The interplay of computational complexity and memory load during quantifier verification

Formal analysis of the minimal computational complexity of verification algorithms for natural language quantifiers implies that different classes of quantifiers demand the engagement of different cognitive resources for their verification. In particular, sentences containing proportional quantifiers, e.g. “most”, provably require a memory component, whereas non-proportional quantifiers, e.g. “all”, “three”, do not. In an ERP study, we tested whether previously observed differences between these classes were modulated by memory load. Participants performed a picture-sentence verification task while they had to remember a string of 2 or 4 digits to be compared to a second string at the end of a trial. Relative to non-proportional quantifiers, proportional quantifiers elicited a sentence-internal sustained negativity. Additionally, an interaction between Digit-Load and Quantifier-Class was observed at the sentence-final word. Our results suggest that constraints on cognitive resources deployed during human sentence processing and verification are of the same nature as formal constraints on abstract machines.
ChatGPT Artificial Intelligence Integration in Science Learning Media: Systematic Literature Review

Artificial Intelligence technology enables organizations to support and improve knowledge management practices. From an AI perspective, knowledge representation defines organizational processes that are familiar with this knowledge. In this era of digitalization, the presence of ChatGPT technology opens opportunities to utilize AI chatbots for education in Indonesia, especially in developing student competencies (skills) needed in the 21st century. Where the purpose of the research is to explain ChatGPT Artificial Intelligence Integration in Science Learning Media. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. We review literature from several publications and analyze the Era of Education 4.0 with the help of computers to improve quality in the industrial sector and also in the field of computer-assisted education. Many innovations can be proposed to improve the quality of learning implementation. GPT chat is a search engine like Google with a form of chat or dialogue. GPT Chat is an information search tool and scientific communication tool.