Reviewerly/exHarmony huggingface dataset:
Annealing-Based Optimization for Selecting Training Space in Ensemble Learning

Ensemble learning integrates many learners to achieve better performance, however, it can increase the complexity dramatically. Redundant learners should be pruned to reduce ensemble complexity while allowing for increased accuracy. There are two common ways to achieve this, which include selecting the optimal training space and the optimal trained learners. This study contributes to reducing redundant learners by selecting the optimal training space using annealing-based optimization. The optimal training space was defined by 1) measuring the similarity between training spaces (clusters) and 2) searching for the best set of training spaces by minimizing the similarity between the selected training spaces. The proposed method was tested on 10 datasets from the UCI repository and compared to five state-of-the-art random subspace methods. The results showed that the proposed method could reduce the size of the ensemble while significantly increasing its accuracy.
Enhancing Communication Efficiency in Adam Optimizer for Distributed Deep Learning

Deep learning is a popular technique used to solve various problems in many domains. Deep learning models consists of learnable parameters. The development of more advanced deep learning models are headed towards increasing the number of parameters. Increase in the number of parameters will also increase communication overhead in distributed deep learning. Therefore, this paper will discuss about existing techniques to reduce communication overhead by reducing the communication rounds and compressing the communications. The reduction in communication uses the idea from CADA [1], while the compression uses the idea from Efficient-Adam [2]. The result are a technique which could reduce communication to as much as 0.97 times that of CADA, while reducing communication data size to 0.29 times the communication size of CADA.
SAPIEN: Affective Virtual Agents Powered by Large Language Models<sup>*</sup>

In this demo paper, we introduce SAPIEN, a platform for high-fidelity virtual agents driven by large language models that can hold open domain conversations with users in 13 different languages, and display emotions through facial expressions and voice. The platform allows users to customize their virtual agent’s personality, background, and conversation premise, thus providing a rich, immersive interaction experience. Furthermore, after the virtual meeting, the user can choose to get the conversation analyzed and receive actionable feedback on their communication skills. This paper illustrates an overview of the platform and discusses the various application domains of this technology, ranging from entertainment to mental health, communication training, language learning, education, healthcare, and beyond. Additionally, we consider the ethical implications of such realistic virtual agent representations and the potential challenges in ensuring responsible use.
Entity Recognition in Arithmetic Word Problem Based on BERT and Boundary Detection

The arithmetic word problem solving is a hot issue in the field of educational technology. The quantitative relation is the key step to solve the problem, and the entity is the base unit in relation. This paper proposes a method of recognizing entities in arithmetic word problem based on BERT model and boundary detection, and combines syntax-semantic (S2) model to extract relations. The first step is to input the problem text into the BERT model to obtain the vector sequence. Then the vector sequence is fed into two different neural networks to identify the boundary of all entities. Thirdly, for each boundary token, the nearest one is combined to obtain the target entity. The identified entities will serve as anchor points to assist the S2 model in extracting quantitative relations. In the experiment, 1500 mixed examples collected previously are used for test. The results show our entity recognition method reaches 77.5% f1 value on simple problem and 74.5% f1 value on complex problem.
Automatic Generation of BFO-Compliant Aristotelian Definitions in OWL Ontologies with GPT

Ontologies are representational artifacts that purport to accurately describe some aspect of reality, including the entities and the relations that hold between them. In computer science, ontologies are software artifacts containing the schematic structure for machine-readable knowledge, typically formed as a graph of subject-predicate-object triples, constrained through Description Logics. These resources and their relations are self-defining, i.e., some resource may be defined by considering all its stated relations. Resources are often attended with natural language annotations, that humans may read and interpret, such as labels and definitions. Many long-standing ontologies have useless lexical definitions that define resources cyclically, e.g., a FOAF: Person is simply defined as “A person”. In Aristotelian terms, the definition of a thing should be reducible, by using terms simpler than itself, such that every definition can be unpacked up to the most general thing, which can only be defined by stating examples and use cases. This paper presents an innovative technique that leverages the Generative Pre-trained Transformer (GPT) large language model, GPT -4, for automatically generating Aristotelian definition annotations for OWL classes that engenders compliance with the Basic Formal Ontology standard.
Recommender System for Social Media: Research Challenges and Future Applications

Social networks have evolved as a ubiquitous means of communication in our daily lives. Users consistently generate a large amount of data on social media platforms, which on the other hand has increased the complexity when selecting relevant social media content. Thus, recommender system has been emerged as a solution to provide personalized and user-centric media recommendations. However, social media has significantly broadened the scope of available inputs such as social sensing data and new types of social media. It not only offers new research opportunities for social media recommender systems, but also indicates that various domains may benefit from the application of social media recommender systems. Therefore, this paper conducts a comprehensive survey of state-of-the-art research between social media and recommender systems. The survey results identify a set of research challenges on how social media recommender systems can be applied in various domains such as healthcare and route planning.
Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory

The policy of the Ministry of Trade of the Republic of Indonesia which sets the highest retail price for cooking oil and then cancels it in early 2022 due to people's complaints about the difficulty of getting cooking oil in the market has received many responses from the public via social media. This study conducts sentiment analysis using Word2Vec and Bidirectional Long Short Term Memory (BLSTM) with the aim of studying the public's response to the removal of the highest retail price policy and effect of both skip-gram (SG) and continuous bag-of-words (CBOW), window size and dimension to the word similarity and accuracy. Data taken via Twitter in March 2022. The data then goes through preprocessing stages, manual labeling by experts, and word embedding using Word2Vec to be further classified using BLSTM. Data labeling produces imbalanced data where the largest is neutral sentiment (53%) followed by negative (27%) and positive (20%). ome of the words such as ‘mafia’, ‘mahal’, ‘langka’, ‘tangkap’, ‘bongkar’, and ‘mendag’ could become the government's attention. The similarity value of the SG model tends to have a higher value than CBOW at a lower word vector dimension. The model that gives the best performance results is the Word2Vec model with 50 dimensions, SG architecture and window size 5. This is shown in the accuracy of 0.71 which is the highest value and only shows slight signs of overfitting. Symptoms of over fitting will be more visible with increasing dimensions.
GPT-4: A Stochastic Parrot or Ontological Craftsman? Discovering Implicit Knowledge Structures in Large Language Models

Ontologies are representational artifacts that purport to accurately portray the aspect of reality under the purview of the ontologists laboring upon them. Ontologies exist in a spectrum of formality, from lexical thesauri to knowledge graphs, to collections of statements of first-order logic. The recent proliferation of Large Language Models (LLMs) has brought to bear interactive “knowledge bases” with general awareness of most things. As ontologists create ontologies from their understanding of reality; and as LLMs, presumably, possess some “understanding” of reality, embedded in their vector matrices corresponding to lexical terms from massive quantities of learned texts, a question is posed: what form of ontology can an LLM create when prompted about some novel facet of reality, without explicitly asking it for an ontology? I.e., will an LLM categorize things into bins, or a subsumption hierarchy, or perhaps something else? LLMs, as they are understood, respond when prompted with the most likely response, because they are predictors of next tokens, i.e., they are stochastic parrots. In any case, it is posited that, if prompted without any explicit request for an ontology, an LLM can produce an ontology of novel form, effectively granting insight into the “understanding” an LLM has of the world, as all humans possess an understanding of the world that ontologies are based upon. This paper explores the use of the flagship LLM, GPT-4, in forming an ontology of a novel domain.
MicroVM on Edge: Is It Ready for Prime Time?

Container virtualization is recognized as indispensable for realizing the vision of edge computing due to its advantages. However, OS-level virtualization suffers from a relatively low degree of security. Recently, microVM technology has emerged in response to this deficiency to provide stronger isolation and security while delivering performance comparable to the containers. In this work, we aim to gain a better understanding of microVM's suitability for edge computing in comparison with containers. We conduct extensive experiments on diverse workloads to test how microVMs compare against containers in several aspects. Through rigorous measurements and analysis, we extract several important findings. Despite having a more complex architecture than containers, microVMs perform comparably to the containers in terms of I/o performance. MicroVMs can even outperform containers in certain I/O workload types by 69%. Network I/O performance of microVMs can be 3x better than containers. We provide our findings and insights on the performance characteristics of microVMs on edge.
THE IMPACT OF DIGITAL LEADERSHIP ON IMPROVING ORGANIZATIONAL PERFORMANCE IN LIGHT OF THE CORONA PANDEMIC: THE MEDIATING ROLE OF DIGITAL LEADERSHIP - A FIELD STUDY ON THE HOSPITALS OF THE PALESTINIAN MINISTRY OF HEALTH

The study aims to explore the impact of digital entrepreneurship on improving organizational performance in the context of the COVID-19 pandemic. The mediating role of entrepreneurial leadership among administrative and service employees in the Palestinian Ministry of Health is also examined. The study sample consisted of 200 employees working in administrative and service roles in the Ministry of Health in the Gaza Strip. A questionnaire was used as a tool for data collection, and SPSS software was utilized for data analysis. The study arrived at several findings, including a statistically significant impact of digital entrepreneurship on improving organizational performance among employees in administrative and service roles within the Palestinian Ministry of Health. There was also a statistically significant effect for digital knowledge management, digital business environment management, securing financial funding, and electronic leadership skills in enhancing organizational performance. Based on these findings, several recommendations were made. These include appreciating the experiences of employees and deploying them appropriately in work, providing free services to the community surrounding the hospitals, increasing trust between employees and managers through attentive listening and considering employee opinions in decision-making, and developing training programs by launching specialized training courses for medical and administrative staff on the use of digital tools and technologies
Archivist and Archives Management In Digital Era

In this digital era, everything is technology-based, so that Human Resources in carrying out their work must develop according to the speed of development. Archivists are Human Resources in the field of archives that are highly exposed to the development of digital technology. Previously Archive was always identified with the activity of managing old and dusty archives. Whereas Archivists in this era must be able to manage archives both conventionally and digitally or electronically. Archivists in this case must continually improve the quality and competency they possess, one of which is by increasing their capabilities in the field of records management. Archivists must change their way of thinking in managing archives, which were previously based on conventional to electronic based records management. Archivists must be able to adjust to technological developments so they can use computer systems in their work to manage records.
Dialog Komunitas: Menggabungkan Teknologi dan Metodologi Mengajar Inovatif di Sekolah

Integrating technology into the education process is an important step towards the digital age, especially in Jakarta's school environment. "Community Dialogue: Combining Technology and Innovative Teaching Methodologies in Schools" started as a discussion forum aimed at addressing issues and opportunities related to the application of technology in education. The event brought together various stakeholders, including teachers, technology experts and policy makers, to share knowledge and best practices with a focus on implementing innovative teaching methodologies. Participants were invited to work together and help build strategies for using technology in the classroom through group discussions and Q&amp;A sessions. Adaptive curriculum development, the use of digital tools in teaching, and blended learning approaches were some of the topics discussed. The results of the discussion showed that teachers should be trained in educational technology and the curriculum should be adapted to support digital learning. As an effort to improve the quality of education in Jakarta, this discussion emphasized the importance of the involvement of all parties in the effective implementation of educational technology. This dedication is very important, especially in providing direction and guidance for schools in Jakarta to use educational technology that is sophisticated and relevant to the needs of students in the modern era.
Evaluasi Penerapan Belajar E-larning dalam pendalaman materi pada Universitas Wiralodra

The face-to-face learning system is an effective method. However, there are several weaknesses in the face-to-face method, namely limited time for evaluation and discussion, because time is wasted during face-to-face meetings by delivering several courses. The aim of this research is to develop an E-learning prototype at Wiralodra University to complete the face-to-face learning system. Stages in building an E-Learning prototype apply the stages in the prototype model. Stages in building an E-Learning prototype apply the stages in the prototype model. This has three stages, namely receiving opinions from customers, building and improving prototypes, testing and evaluation. Apart from that, data collection techniques include observation, interviews, and building and improving prototypes.
INCREASING THE STUDENTS READING COMPREHENSION OF REPORT TEXT WITH SOCRATIVE APPLICATION

This research aims to find out the increasing of students reading comprehension of report text by using Socrative Application, especially on literal comprehension and interpretive comprehension in 10th grade students of SMA Negeri 14 Makassar.The research applied Pre-Experimental Method using one group pre-test and post-test research and collect data through pre-test and post-test instruments. The sample in this study was X MIPA 4 SMA Negeri 14 Makassar which amounted to 36 students. Samples was taken by using the Purposive Sampling Technique. The data obtained was also analyzed using SPSS Application version 25. Based on the research findings, before utilizing the advantages of the Socrative Application, namely ease of use, educational benefits or instant feedback, and quiz format and assessment of student mean score on literal comprehension is 49.4 and interpretive comprehension is 41.6. After utilizing the advantages of the Socrative Application the average student on literal comprehension becomes 70, while for interpretive comprehension is 51.2. The occurrence of this significant increase in literal comprehension is due to the advantages of the Socrative Application which can increase students' literal comprehension while the absence of significant increase in students' interpretive comprehension occurs due to the difficulty of students inrking conclusions on the Socrative Application.Therefore, based on these results, it is recommended to use Socrative Application at reading comprehension, especially in literal comprehension.
IMPLEMENTING HYPERCORRECTION APPROACH TO REDUCE THE STUDENTS FOSSILIZATION IN ENGLISH PRONUNCIATION AT SMA NEGERI 7 SELAYAR

The purpose based on this research was to find out whether or not the hypercorrection approach reduces the student’s fossilization in English pronunciation at SMA Negeri 7 Selayar. The researcher used a pre-experimental design and quantitative method using one class for a pre-test and post-test.In the 2022–2023 academic year, the population of this study were 32 students of class 10 IPA at SMA Negeri 7 Selayar. To determine the research sample, cluster sampling method is used. The speaking test is used a data collection method.The results show of the study show that the application of the hypercorrection approach can reduce the fossilization of students’ pronunciation in vowels (/i:/, /I/, /e/, /æ/). This is evidenced by using pre-test scores (39.53) and a post-tests score(72.18), which shows that in addition to the test scores, students’ pronunciation in the post-test is higher than the pre-test. (29.10) which is higher based on the t-table value (1.696). As a result, the Null hypothesis was refuted and the alternative hypothesis was adopted. In the study the researcher came to the conclusion that Hypercorrection reduces student’s fossilization of pronunciation.
THE STUDENTS’ PERCEPTION OF CANVA APPLICATION AS A TOOL IN LEARNING WRITING SKILL

The use of technology in English language learning can bring tremendous effects because it provides students with enjoyable learning opportunities and facilitates the development of their English language skills. In this context, the study aimed to investigate students’ perceptions of using the Canva application as a tool for learning writing skills in the explanatory text, namely cause and effect in SMA Negeri 11 Semarang in the academic year 2022/2023. The study was conducted using qualitative research. The sample of the respondents was XI MIPA 6 with a total of 36 students. The sampling technique used purposive sampling. A survey technique was used to collect the data by distributing the proposed questionnaire to the respondents through Google Forms. A questionnaire was used as an instrument consisting of 3 indicators requiring 10 items to obtain data related to measure students’ viewpoints on the utilization of Canva in learning writing skills. This study revealed that the Canva application proved to be a valuable resource in assisting students in cultivating their writing skills and it is regarded as a user-friendly resource since it can be conveniently reached via smartphones and personal computers. The features of Canva empower learners to exhibit their creativity in writing by using a wide variety of templates. It concludes that students can improve the quality of their writing by using Canva as a technological medium in the 21st century. Canva is a fascinating and well-chosen application and can be viewed by students as a valuable and functional learning tool to boost their writing.
Application of the PCA Method to Identify Factors Affecting Poverty Rates in West Kalimantan

Poverty is one of the most challenging problems faced by developing countries like Indonesia. Poverty is influenced by various factors such as the economy, education, population, and several other factors. In this study, factors that affect the poverty rate in West Kalimantan are identified using the Principal Component Analysis method. To identify existing factors and determine the causes of poverty in West Kalimantan, it is necessary to compact the data without changing the significance of the data. The factors used are education and economic factors consisting of thirteen variables that are thought to affect poverty. Based on the Principal Component Analysis method of thirteen variables, eight reduced variables were obtained that can be used to identify poverty. From the eight variables used, three main components were formed that affect the poverty rate in West Kalimantan with a total variance value of and a correlation value of more than for each component. This shows that the poverty rate in West Kalimantan can be identified with the main components formed to explain the factors used.
The Role of the Library in Fostering Students’ Reading Habit: A Case in an Islamic Primary School

Reading interest and reading skills are inextricably linked, fostering a reciprocal relationship that enhances overall literacy and comprehension. Cultivating a strong reading foundation from an early age is crucial, as it lays the groundwork for academic achievement, professional advancement, and personal fulfillment. This study delved into the realm of reading interest and library activities at MI Salafiyah Duwet Pekalongan City, seeking to unravel the intricate connections between these factors and their impact on student learning. The findings revealed that reading interest plays a pivotal role in enabling students to attain their educational objectives and refine their reading proficiency. Furthermore, the study underscored the library's transformative influence on fostering a passion for reading. Students who actively engage with library resources expand their knowledge base, cultivate a deeper appreciation for the written word, and develop a lifelong love of reading. Effective library management encompasses a three-pronged approach: organizing, mobilizing, and building. Organizing involves meticulously crafting a comprehensive library plan that encompasses the collection, staffing, and physical space. Mobilizing entails actively promoting the library's resources and services to the student body and the wider community. Building centers on maintaining the library's infrastructure, ensuring its continued functionality, and implementing necessary improvements to optimize its effectiveness.