Reviewerly/exHarmony huggingface dataset:
Adversarially Trained Actor Critic for offline CMDPs

We propose a Safe Adversarial Trained Actor Critic (SATAC) algorithm for offline reinforcement learning (RL) with general function approximation in the presence of limited data coverage. SATAC operates as a two-player Stackelberg game featuring a refined objective function. The actor (leader player) optimizes the policy against two adversarially trained value critics (follower players), who focus on scenarios where the actor's performance is inferior to the behavior policy. Our framework provides both theoretical guarantees and a robust deep-RL implementation. Theoretically, we demonstrate that when the actor employs a no-regret optimization oracle, SATAC achieves two guarantees: (i) For the first time in the offline RL setting, we establish that SATAC can produce a policy that outperforms the behavior policy while maintaining the same level of safety, which is critical to designing an algorithm for offline RL. (ii) We demonstrate that the algorithm guarantees policy improvement across a broad range of hyperparameters, indicating its practical robustness. Additionally, we offer a practical version of SATAC and compare it with existing state-of-the-art offline safe-RL algorithms in continuous control environments. SATAC outperforms all baselines across a range of tasks, thus validating the theoretical performance.
Comparative Analysis of Anomaly Detection Techniques Using Generative Adversarial Network

Anomaly detection in a piece of data is a challenging task. Researchers use different approaches to classify data as anomalous. These include traditional, supervised, unsupervised, and semi-supervised techniques. A more recently introduced technique is Generative Adversarial Network (GAN), which is a deep learning-based technique. However, it is difficult to choose one anomaly detection algorithm over another because each algorithm stands out with its own performance. Therefore, this paper aims to provide a structured and comprehensive understanding of machine-learning based anomaly detection techniques. This paper carries out a survey of the existing literature on machine learning-based algorithms for anomaly detection. This paper places a special emphasis on Generative Adversarial Network-based algorithms for anomaly detection, since it is the most widely used machine-learning based algorithm for anomaly detection.
Sentiment Classification of Community towards COVID-19 Issues on Twitter (Case Study: Indonesia, March-May 2020)

This study examines sentiment analysis related to COVID-19 in Indonesia (March-May 2020) using InSet Lexicon as training data in supervised machine learning models. The dataset comprises 7,967 tweets, divided into 90% training data and 10% testing data. The results reveal that Support Vector Machine (SVM) and Random Forest (RF) are the most effective methods, achieving accuracy above 80%, with SVM reaching 87% and RF at 86%. InSet Lexicon itself attains an accuracy of 75%, a macro average of 69%, and a weighted average of 74%, making it an effective alternative for large-scale data labeling. Research recommendations support further development of InSet Lexicon for sentiment classification and expansion of the lexicon for foreign languages to enhance sentiment analysis accuracy in a global context. This study provides valuable insights into understanding public sentiment regarding crucial issues such as COVID-19 in Indonesia.
Integrating Satellite Imageries and Multiple Geospatial Big Data for Granular Mapping of Spatial Distribution of Human Development Index in East Java, Indonesia

The availability of data on the Human Development Index (HDI) is crucial as a gauge of regional performance, particularly in terms of assessing the development of human resources. In Indonesia, the collecting of HDI data usesthe conventional method, such as undirect estimation, National Socio-Economic Survey (SUSENAS), The Ministry of Religion, or inventory of sectoral data that used the large cost, time, and effort. Additional data are required to provide more detailed poverty data at a lower cost and with more recent information to overcome these limitations. According to recent studies, the quality of life for measuring HDI can be identified down to the granular level using geospatial big data. Therefore, the contribution of this research is to implement the use of geospatial big data, such as integrated multi-source satellite imagery data and Point of Interest (POI). Besides that, this study develops the relative spatial human development index in 11 km x 11 km resolution for the granular mapping of the quality of life to measure the HDI in East Java, Indonesia. The kinds of weighted sum models used in this study such as equal weight (EWS), Pearson (PCCWS), Spearman (SCCWS) correlation-based weight, and Principal Component Analysis (PCA)-based weight (PCAWS). The best RSHDI PCCWS for representing the human development index in East Java in 2022, which was determined using a weight-sum model based on Pearson correlation, has a correlation coefficient of 0.7858 (p-value = 5.078 x 10-9) and is highly correlated with official HDI data. The use of this RSHDI as a predictor variable in the estimation of HDI data shows the ideal model had an RMSE of 3.098% and an R2 of up to 61.75% using RSHDI PCCWS. According to the findings of the descriptive analysis of this map, areas with low RSHDI scores typically in some regencies areas in Madura Island and the east area of East Java with geographically depressed, while areas with high RSHDI scores typically have dense populations and have better accessibility such as urban area in Surabaya and Kota Malang. As a result, the official human development index data can be supported by the RSHDI's ability to map spatially deprive areas
The Perceptions of Students and Teacher Challenges in Utilizing “Kejar.Id” at One of Vocational School in Serang Regency

The purpose of this study is to determine student perceptions and challenges faced by a teacher when using Kejar.id during the Covid-19 pandemic. This article uses qualitative descriptive methods with interviews, questionnaires, and documentation techniques to collect data. The data have collected from the twelfth-grade students and the English teacher which have learned using Kejar.id in English subject. The researcher himself is the instrument of this research to analyze the data from participants. The research findings showed that mostly students gave good perceptions about Kejar.id. The students said that the use of Kejar.id helped them in terms of student administration, checking learning progress, accessing virtual classes, the learning process and assessment, meanwhile, the problem experienced by students was the network. Furthermore, the results of interviews conducted with the teacher showed that there are four problems arose in using Kejar.id, namely teaching learning styles and culture challenges, pedagogical e-learning challenges, technological challenges, and time efficiency challenges. The results of this research indicated if students gave the positive perceptions in using Kejar.id also the teacher have faced several challanges when using Kejar.id as learning management system in English classroom
Performance Comparison of Gradient Boosting and Random Forest for the Classification of False News

The rapid increase in the utilization of social media for news browsing is attributed to its capacity, affordability, improved attractiveness, and accessibility, leading to the dissemination of "fake news." The widespread propagation of fake news has unintended negative effects on individuals and culture. Certain individuals exploit social media platforms to disseminate false information, seeking attention and reaping monetary and political advantages. To effectively distinguish between authentic and false news, intelligent solutions are required. The Gradient Boosting Classifier and Random Forest Classifier are the two ML classifiers that we compare and contrast in this research work. In comparison, the Gradient Boosting Classifier outperforms the Random Forest Classifier, achieving an impressive accuracy of 92% in classifying fake news.
Improved tweets in English text classification by LSTM neural network

This paper analyzes the performance of an LSTM-type neural network in the sentiment analysis task in tweets in English about the COVID-19 pandemic. Primarily, the organization and cleaning a database of tweets about the COVID-19 pandemic is performed. From the original database, two other databases through different discretizations of the polarities of the tweets using Heaviside-type functions are created. Vectorization of tweets using the Word2Vec word embedding technique is carried out. Computational implementations of LSTM neural networks to the context of our research problem are adapted. Analyzes and discussions on the feasibility of the proposed solution taking into account different types of hyperparametric adjustments in the neural network models is carried out. Publicly available databases organized through the Mendeley Data public data repository are used.
Pengaruh Sistem Informasi Akuntansi, Pengendalian Internal, dan Kompensasi Terhadap Kecurangan Akuntansi Dalam Sistem Pemberian Kredit Pada PT BNI Kota Metro

This study aims to determine the effect of accounting information systems, internal controls and compensation for accounting fraud in the credit granting system at PT BNI Kota Metro. The type of research used in this research is quantitative research with an associative approach. Data analysis was carried out by testing instruments, classical assumptions and testing hypotheses. The results of the study show that the Accounting Information System has a significant effect on accounting fraud. Internal control has a significant effect on accounting fraud. Compensation has a significant effect on accounting fraud. Accounting Information Systems, Internal Control, and Compensation simultaneously have a significant effect on accounting fraud.
Kontekstualisasi pemikiran S. R. Ranganathan dalam perkembangan dunia perpustakaan masa kini

Science is dynamic so it continues to develop over time. A number of figures contributed to coloring scientific dynamics, one of which was S.R Ranganathan who dedicated his life to the world of libraries. His name is well known as the Father of Libraries in India and his ideas spread throughout the world. His magnum opus Prolegomena to Library Classification and Colon Classification stimulated the birth of the Dewey Decimal Classification. Apart from that, his work as outlined in the Five Laws of Library Science had a major impact on the development of the world of libraries. This research uses a qualitative research method with a type of literature study (library research), then the data that has been obtained will be followed by reading and note-taking techniques and then processed the data analytically and descriptively. This article aims to find out the contextualization of S.R Ranganathan's five laws in the development of today's library world. These five laws read: Books are for use, Every reader his or her book, Every book its reader, Save the time of readers, and The library is a growing organism are still relevant today which is synonymous with technology and digitalization.
Motivation and Countermeasures of Financial Fraud in Listed Companies A Case Study of Luckin Coffee

The prevalence of financial fraud within publicly traded corporations has emerged as a significant concern within the field of auditing. In recent years, there has been an exacerbation rather than a mitigation of instances of financial fraud. Luckin Coffee is a domestic coffee brand that has emerged in the market, established by Qian Zhiya, who previously served as the CEO of Shenzhou Youchuan Group. Established in the year 2017, the organization achieved a notable milestone by being successfully listed on the NASDAQ stock exchange in 2019. In the year 2020, the disclosure of financial wrongdoing occurred. The discreet and innovative financial fraudulent practices employed by Luckin Coffee can serve as a valuable point of reference for the future advancement of firms. This paper presents an introduction to the fundamental notion of financial fraud and its associated theories. It proceeds to investigate the specific financial fraud process employed by Luckin Coffee. Additionally, it proposes recommendations for mitigating the risk of financial fraud in listed businesses, utilizing the Gone theory.
Examining Biogas Potential from Rotting Fruits for Advanced Waste Management, Environmental Conservation, and Sustainable Energy Generation

This research aims to analyze the efforts of Koperasi Gemah Ripah Yogyakarta in converting rotting fruits into biogas as part of waste management at Gamping Fruit Market. The methodology involved interviews, field studies, and a comprehensive literature review from various journals. The research findings are threefold. Firstly, Koperasi Gemah Ripah employs a systematic approach to waste management comprising collection, transportation, and disposal. Secondly, the biogas produced through the processing efforts made by Koperasi Gemah Ripah serves as an alternative energy source, generating electricity. Thirdly, the collaborative partnership between Koperasi Gemah Ripah and the Waste Refinery Center at Universitas Gadjah Mada has been proven to be successful in innovatively managing waste by converting it into biogas. In conclusion, the research underscores the effective waste management strategies implemented by Koperasi Gemah Ripah, showcasing the potential of biogas as a sustainable solution to mitigate environmental damage.
Quizizz Media to Improve Students' Motivation and Storytelling Ability at SD Muhammadiyah Purworejo

This research aims to determine the increase in students' motivation in participating in learning and language skills, especially telling stories using Quizizz media at Muhammadiyah Elementary School, Purworejo. This type of research uses mixed research methods, namely using qualitative methods to measure motivation and using quantitative methods to measure the increase in students‘ storytelling abilities. The subjects in this research were students at SD Muhammadiyah Purworejo. Data collection uses interview, observation, and test related to the Quizizz media learning process. Data analysis techniques use descriptive statistics and inferential statistics. This media improved the students‘ storytelling ability and their motivation in learning. It received a positive response for its use in the learning process so it is suitable as a solution to problems that arise for students. They become more enthusiastic and enthusiastic, so they can eliminate feelings of boredom and laziness while learning from home because it provides a feeling of fun, comfort, and humor in learning. So that their intellectual intelligence can develop well.
Gender differences in human capital accumulation

This comprehensive study explores the intricate dynamics of gender disparities in human capital accumulation, delving into the realms of education, skills development, and career progression. Through an extensive literature review spanning economics, sociology, and gender studies, this research synthesizes empirical findings and theoretical frameworks to illuminate the multifaceted factors shaping divergent pathways for men and women.The analysis reveals persistent gaps in educational access and attainment, highlighting systemic challenges that hinder women's pursuit of certain academic disciplines. These disparities extend into the professional arena, where societal norms and workplace biases contribute to differential skill development and career trajectories.This paper not only identifies the economic implications of such disparities, affecting individual earning potentials and overall workforce productivity, but also underscores the broader societal impact on national economic growth.As we navigate the complexities of gender differences in human capital accumulation, the study concludes by advocating for proactive policy interventions. By addressing systemic issues and fostering inclusive environments, we can strive towards dismantling barriers, promoting equitable opportunities, and advancing a more just and economically prosperous society.
ChatGPT: The New Trend of Smart Education

In response to the limitations of the traditional education and teaching model, this article proposes a smart education model based on ChatGPT. The model actively breaks the constraint of time and space and the design patterns of traditional education, providing smart education services including personalized learning, smart tutoring and evaluation, educational content creation support, and education big data analysis. Through constructing an open and inclusive learning space and creating flexible and diverse educational models, ChatGPT can help to meet students’ individuality and overall development, as well as assist teachers in keeping up with the students’ learning performance and developmental requirements in real-time. This provides an important basis for optimizing teaching content, offering personalized and accurate cultivation, and planning the development path of students.
Problems Encountered by Bachelor's Level Students in Reading English Texts

Reading textbooks is challenging as our school-level courses do not teach students how to improve their reading comprehension abilities. They simply memorize the word meaning and question-answers. Also, they have no idea in improving their reading skills. Reading comprehension is still crucial for their academic and personal success but it is being ignored which motivated me to research this issue. This study aimed to explore reading problems encountered by bachelor-level students in English texts. I selected four students studying compulsory English at the bachelor level as the participants purposively from two constituent campuses of TU in Rupandehi. I adopted the phenomenological research method and in-depth interviews as major research tools for collecting information using the thematic analysis technique. None of the previous research is expressive and specific on this issue so, I intend to explore it. This paper reveals students' limited and unfamiliar vocabulary, lengthy texts, unfamiliar and uninteresting text, lack of inference skills, problems with reading fluency, lack of background knowledge about the text, inadequate intensive reading practices, lack of exposure to printed materials, complex sentence structure, and lack of reading habit as problems in understanding of English texts. Hence, it may contribute to enhancing the problems that bachelor-level students face in reading English texts.
Optimizing The Use Of Digital Libraries In Strengthening Educational Infrastructure In The Era Of Information Disruption

Optimizing the using of digital-based libraries (digital libraries) is a major transformation in the world of education today. Digital libraries offer many conveniences and diverse connectivity. This research aims to analyze opportunities for using digital libraries in the era of information disruption. The research results show that in the era of information disruption, the use of digital-based libraries is very helpful, especially in supporting learning activities. Digital libraries are easy to access, portable, economical, rich in references, equipped with searching tools, keywords and knowledge sharing. This research uses a qualitative approach.
PENGELOMPOKAN SAHAM MENGGUNAKAN K-MEANS DALAM PEMBENTUKAN PORTOFOLIO OPTIMAL

K-Means clustering analysis is a technique used in grouping objects that have similar characteristics. In forming a portfolio, investors need a group of stocks from different sectors that aim to build a well-diversified portfolio. Portfolio diversification is the placement of assets from various stocks in such a way that risks can be minimized. This study aims to obtain the results of grouping stocks with K-Means at IDX80 and then determine the optimal portfolio of each cluster formed using the Mean Variance method in the period January, 1st 2020 to November, 10th 2022. As a result, obtained in this study that grouping with K -Means produces four groups and is the best portfolio consisting of 10 stocks with a Sharp ratio performance value of 0.0062 with a risk portfolio of 1.59% and an expected return portfolio of 0.17%.
STUDENTS’ REFLECTION TOWARD LEARNING VOCABULARY USING DUOLINGO APPLICATION

Vocabulary plays an essential role for the students in acquiring a language because it is used to express thoughts, ideas, and information. However, due to its broad materials and time limit provided by the school, vocabulary cannot be studied optimally. As for it, the students should be more aware to learn the vocabulary independently, for example by utilizing the use of online language learning application, such as Duolingo. Therefore, this research is conducted to investigate the students’ reflection regarding their vocabulary learning using Duolingo Application. The participants of this research are three junior high school students in Cimahi, because the students meet the Duolingo’s curriculum target. The method of the research is a case study design. To acquire the data needed in this study the instruments used are interview and observation. From all the data obtained, researcher finally concludes that the students reflect positive attitudes toward the Duolingo application due to its advanced features. The students become more challenged to play the application and feel less worry because Duolingo provides them with many immediate feedbacks, well-structured materials and appreciation tags which can ease, motivate and reassure the students to achieve their learning goals. Nevertheless, the students face some drawbacks, such as the unnatural sentences, repeated questions, and lack of grammar guide. Even though, the students use the third party to manage the problem emerged.
THE ANALYSIS OF ENGLISH SLANGS FOUND IN THE ANTMAN MOVIE

An educational program is a basic reference document for preparing the learning process. The teacher is the person who implements the program that has been determined. The educator's ability to use learning models in teaching can determine whether students are motivated to learn. Edutainment is a learning process designed to combine educational and entertainment content in harmony, making learning activities enjoyable. Entertaining learning is a learning model that includes a series of learning theories that involve students in learning activities that are fun and not boring. Some edutainment learning models that teachers can use are cooperative learning, multiple intelligence learning, PAKEM learning, quantum learning, and CTL (Contextual Teaching Learning )