Reviewerly/exHarmony huggingface dataset:
TANTANGAN PENERAPAN REKOD DAN ARSIP ELEKTRONIK BAGI ORGANISASI

The purpose of this article is to find out how the challenges of applying electronic records and archives are in accordance with existing rules and requirements. Using a qualitative model with literature studies about the applications and challenges for an organization in managing electronic records and records in other countries. In the Indonesian context, the application of electronic records and archives, especially in the archive creation stage, is highly dependent on the readiness of the organization itself, both in policies, budgets, and the readiness of human resources. The practical objective of this article is to provide a comprehensive understanding of how to apply electronic records and archives and the stages in overcoming existing challenges. So that when practically applying all obstacles can be overcome properly.
MANAJEMEN RISIKO: TINJAUAN REGULASI KEARSIPAN

This study aims to examine the application of risk management in the archive sector. This article provides an overview of the application of risk management listed in the four regulations governing archives management, especially those issued by the National Archives of the Republic of Indonesia. Based on the results of the analysis of the four regulations, the following results were obtained as follows: Regulation of the head of the National Archives of the Republic of Indonesia number 31 of 2015 concerning the establishment of an archive depot, Regulation of the Head of the National Archives of the Republic of Indonesia Number 2 of 2014 concerning Guidelines for Official Manuscripts, Regulation of the Head of the National Archives of the Republic of Indonesia Number 23 2015 concerning Protection and Rescue of Archives from Disasters and Regulation of the Head of the National Archives of the Republic of Indonesia Number 4 of 2019 concerning Guidelines for the assessment of paper archives. Records management involves risk management to reduce the risk impact of an event. The government has provided regulations that need to be observed together by archive managers.
Research on fault diagnosis model driven by artificial intelligence from domain adaptation to domain generalization

Transfer learning is studied through inductive research, knowledge transfer, lifelong learning and cumulative learning. The research scope and field of transfer learning are very wide. Transfer learning is a training method that can transfer the network structure and weight originally used to solve the mature task methodology to the new learning task, and can also get better results in the new task. The reason why transfer learning can be realized is that the characteristics of convolutional neural network in shallow learning are universal. In the case of insufficient samples, transfer learning can be used to transfer these general feature learning from other trained networks, so as to save training time and obtain better recognition results.
Digital signal processing techniques for image enhancement and restoration

Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.
PEMANFAATAN LITERASI DIGITAL DALAM PENINGKATAN SKILL PEMROGRAMAN

The dissemination of technological, scientific, social and cultural advances is very important to inform the community so that the implementation of learning activities is integrated with the educational curriculum. The development of science and technology must be applied directly and precisely to the school environment. Policy holders carry out strict supervision of school activities, especially so that students wisely, creatively and responsibly use digital devices. Achievement-based learning expands space for activities in formulating learning objectives. This activity applies the teaching model of lectures and practice (demonstrations). The fundamental role of the target audience is to increase the use of digital literacy media as a learning tool. The target of the activities for Grade 10 SMA Gajah Mada students aims to motivate students to explore their potential and talents by utilizing internet media as a learning resource according to the topics of interest. This activity utilizes digital hard and soft computer media.
Research on methods and applications of question answering system in the context of ChatGPT

In the 21st century, there has been a growing importance placed on the "body" of artificial intelligence, particularly as it relates to language processing. Researchers have developed various machine learning models with a focus on language understanding, including Large Language Model (LLM), Bidirectional Encoder Representation from Transformers (BERT), and Natural Language Processing (NLP). These models have led to the development of numerous applications, such as ChatGPT-3.5, which has recently gained widespread attention. In addition to ChatGPT, other applications have also benefited from these language processing models, including Question Answering Systems (QAS). This paper will examine three QAS that have been enhanced by the context of ChatGPT, discuss the relevant applications, and analyze these different applications in order to predict future trends in this field. One notable QAS is OpenAI's GPT-3-powered AI that can answer questions about any topic. This application leverages the capabilities of GPT-3 to provide accurate and informative responses to a wide range of questions. Another QAS is IBM's Watson, which utilizes natural language processing and machine learning algorithms to understand and respond to user queries. Watson has been used in various industries, including healthcare, finance, and retail. A third QAS is Google's BERT-based system, which uses pre-trained language models to improve its responses to user queries. This system has been integrated into Google Search and other products, allowing users to receive more precise and relevant search results. Overall, the development of these QAS and other language processing applications marks an exciting period of progress in the field of artificial intelligence. As researchers continue to refine these models and explore new applications, we can expect to see even more advanced and sophisticated language processing systems emerge in the future.
THE EFFECTIVENESS OF CREATIVE WRITING ON STUDENTS’ DESCRIPTIVE PARAGRAPH WRITING ABILITY AT THE ELEVENT GRADE STUDENTS OF SMAN 11 PALEMBANG

The title of this study is The Effectiveness of Creative Writing on Students’ Descriptive Paragraph Writing Ability at the Elevent Grade Students of SMAN 11 Palembang. The formulation of this study the effectiveness of creative writing on descriptive paragraphs against the writing abilities of students in class XI SMAN 11 Palembang. The writer used a pretest-posttest consisting of an experimental class and a control class, meaning the writer selected the group to be randomly studied to find out the differences between the two groups. Students of Class XI IPS 3 and XI IPS 4 are selected classes to be used as samples, each consisting of 35 students. The data collected was taken from a written test. The average pre-test score for the experimental class was 64.25, whereas the post test score was 70.08, as well as the average pretest score in the control class was 63.51 while the posttest score was 64,00. Results from the two groups above showed a positive response to the student’s ability to write descriptive paragraphs. The results of this research showed that creative writing on student writing ability had a low score at the time of the pretest. After treatment, student writing ability improved significantly. It can be seen from higher student scores than before. Data from the writing test had a positive result against student creative writing in their writing ability. Keywords:Creative writing, descriptive paragraph, writing ability
AI and Humans: Friends or Foes?

The advent of large language models (LLMs), has raised a strong debate across different academic fields, as well as in the general media. As a form of general purpose artificial intelligence tool, it has split the public opinion into two rather opposing camps: One that believes that it is a technology that will benefit humankind as a whole, by making us more efficient in producing texts and in other creative tasks and the other that sees it as an existential threat to our species once it acquires the ability to self-improve beyond human cognitive abilities. In this article, we look at the short history of humans and information technology, and discuss some of the benefits and risks of recent AI developments and the impact it is already having on how we understand the frontier between human and machine intelligence.
A Comparative Study On Speaking Skill Between Extrovert And Introvert Students At The Second Grade Of SMAN 3 Bukittinggi

The aims of this study were to know there was a difference between the extrovert and introvert students’ on their speaking skill and which one was better on speaking skill at the second grade students of SMAN 3 Bukittinggi. This research used descriptive quantitative method. The instrument of this research were questionnaire for classified the students based on their personality and speaking test for get their speaking score . The population was the second grade students at SMAN 3 Bukittinggi, meanwhile sample were 30 students who had been assigned purpossively. The finding showed; (1) There was difference between mean of extrovert students, which was (76.5) and the mean of introvert students, which was (82). (2)The result of ttes was -1,980 and it was smaller than ttable in the degree of significance 5% (2,048 &gt; -1,980). The result showed that the students’ personality difference had no statistically difference to the students’ speaking skill. Based on the result of the research, the alternative hypothesis (Ha) was rejected and null hypothesis (Ho) was accepted. In conclusion there was no difference between extrovert and introvert students in speaking skill of the second grade students of SMAN 3 Bukittinggi.
GPTFX: A Novel GPT-3 Based Framework for Mental Health Detection and Explanations

This paper introduces a novel approach GPTFX, an AI-based mental detection with GPT frameworks. This approach leverages GPT embeddings and the fine-tuning of GPT-3. This approach exhibits superior performance in both classifying mental health disorders and generating explanations with accuracy of around 87% in classification and Rouge-L of around 0.75. We utilized GPT embeddings with machine learning models for the classification of mental health disorders. Additionally, GPT-3 was fine-tuned for generating explanations related to the predictions made by these machine learning models. Notably, the proposed algorithm proves well-suited for real-time monitoring of mental health by deploying in AI-IoMT devices, as it has demonstrated greater reliability when compared to traditional algorithms.
Pruning replay buffer for efficient training of deep reinforcement learning

Reinforcement learning (RL) is a type of machine learning that develops artificial intelligence by training an algorithm through multiple generations to understand what strategies to use in various situations. RL has applications in virtually every field, from transportation to research. However, RL is limited in that it is very resource intensive, partially because of the necessity of a large replay buffer, which contains the data learned from each episode. This study provides knowledge on replay buffer reward mechanics to inform the creation of new pruning methods for improving RL efficiency. Specifically, we develop a novel approach designed to reduce storage complexity of the replay buffer and training data and thus improve model efficiency. We create three algorithms, Threshold Replay Buffer Pruning (TRBP), Cluster Replay Buffer Pruning (CRBP), and Inverse Threshold Replay Buffer Pruning (ITRBP), for this purpose, testing three contradicting theories on reward mechanics. We hypothesized that TRBP’s theory would be the most conducive to real-world conditions, which our results corroborated. These results indicated that TRBP can achieve a 2-fold reduction in replay buffer size with only a 5% reduction in score, while CRBP and ITRBP performed much worse. This supported the hypothesis that TRBP’s reward thesis is the most accurate out of the three algorithms, as well as demonstrated that TRBP is a potentially effective replay buffer pruning algorithm.
Optimizing Pix2Pix GAN with Attention Mechanisms for AI-Driven Polyp Segmentation in IoMT-Enabled Smart Healthcare

This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing prevalent challenges in conventional segmentation methods, such as variable polyp appearances, inconsistent image quality, and limited training data, our model significantly augments the precision and reliability of polyp segmentation. The integration of an attention mechanism enables our model to meticulously focus on the intricate features of polyps, improving segmentation accuracy. A unique training strategy, employing both real and synthetic data, is adopted to ensure the model's robust performance under a variety of conditions. The results, validated through rigorous tests on multiple public colonoscopy datasets, indicate a notable improvement in segmentation performance over existing state-of-the-art methods. Our model's enhanced ability to detect critical details early plays a pivotal role in proactive colorectal cancer detection, a key aspect of smart healthcare systems. This work represents an effective amalgamation of advanced AI techniques and the Internet of Medical Things (IoMT), signifying a noteworthy contribution to the evolution of smart healthcare systems. In conclusion, our attention-enhanced Pix2Pix GAN not only offers efficient and reliable polyp segmentation, but also showcases considerable potential for seamless integration into remote health monitoring systems, underlining the increasing relevance and efficacy of AI in advancing IoMT-enabled healthcare.
Content Validity of Questionnaire on the Influence of Housing Affordability Factors on the Well-Being of the B40 Group Using the Content Validity Ratio (CVR)

This article discusses content validity to evaluate research questionnaires. All items are built based on the adaptation of several management theories and models and literature studies obtained from inside and outside the country. This study aims to examine the content validity of a questionnaire on the influence of housing affordability factors on the well-being of life for the B40 group on the East Coast using the Content Validity Ratio (CVR). Quantitative measurement methods through Content Validity Ratio (CVR) and Content Validity Index (CVI) have been used to assess whether the items need to be retained or dropped. Twelve experts were selected to evaluate and confirm the content of the questionnaire. This questionnaire involves things with key factors. The study results show that this questionnaire has good content validity and proves that this questionnaire has excellent potential to be used to identify the influence of housing affordability factors on the well-being of life for the B40 group in peninsular Malaysia using the content validity ratio (CVR).
HCL: Hierarchical Consistency Learning for Webly Supervised Fine-Grained Recognition

Webly supervised fine-grained recognition aims to distinguish subordinate categories (e.g., bird species) with freely available web data. It has significant research and application value for alleviating the costly professional manual annotations' dependence in the fine-grained recognition task. Nevertheless, there exists label noise in web data to decrease the model's recognition performance. Most existing methods attempt to select clean data via loss analyses, which favors easy samples to hinder mining subtle differences contained in hard samples. Inspired by the intrinsic trait of consistent semantic predictions among different hierarchies of clean samples in fine-grained recognition, we propose a hierarchical consistency learning (HCL) approach for detecting noisy samples and capturing multi-hierarchy discriminative clues simultaneously. Specifically, our HCL approach works in a coarse-to-fine order, which first explores the semantic consistency between the image level and object level through prediction distribution conformance analyses. The open-set noise (i.e., samples irrelevant to any fine-grained subcategory) is thus detected, and the visual object information is highlighted with image-object contrastive learning. Then, the semantic consistency between object-level and part-level prediction distributions is utilized for detecting closed-set noise (i.e., samples mislabeled as other fine-grained subcategories), and local discriminative information is enhanced with object-part contrastive learning. Extensive experiments and analyses on three widely-used webly supervised fine-grained benchmark datasets demonstrate that the proposed HCL approach can achieve new state-of-the-art. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/PKU-ICST-MIPL/HCL_TMM2023</uri> .
A Security Model of Multihospital FHIR Database Authorization Based on Secret Sharing and Blockchain

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing n shares in the way that a set of any k shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in data security field. Recently, the fast healthcare interoperability resources (FHIR) standard is defined for medical data exchange across various medical institutions. In this paper, we combine SS, ElGamal’s encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal’s private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in private network, we can recover ElGamal’s public key from a deployed smart contract on blockchain and recover ElGamal’s private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization.
Bank Loan Fraud Detection with Integrated KYC Verification System

In today's technologically-driven financial landscape, bank loan fraud poses a significant threat to both financial institutions and their customers. While traditional methods of fraud detection have been moderately effective, the increasing sophistication of fraudsters necessitates the implementation of more advanced measures. This research proposes an integrated system for bank loan fraud detection that leverages the robustness of Know Your Customer (KYC) verification. By combining traditional KYC processes with advanced machine learning algorithms, this system seeks to provide a more comprehensive approach to detecting and preventing fraudulent loan applications. Initial results indicate a marked reduction in successful fraud attempts, as well as a decrease in false positives compared to conventional systems. Furthermore, the integrated system offers enhanced customer experience by streamlining the loan application process, reducing verification times, and ensuring greater security of personal data. As financial institutions continue to grapple with the challenges of fraud, this research underscores the importance of integrating traditional verification methods with cutting-edge technological solutions for optimal results
Adolescent Health Education in Don Bosco Frater Junior High School Students Tomohon City

Adolescents frequently encounter several nutrition-related issues, such as anemia, obesity, and chronic energy deficiency, among others. During the developmental phase of adolescence, individuals need guidance as well as encouragement from adult figures. There is a need for guidance in order to enhance individuals' self-awareness and awareness towards regularly arising concerns. Health education is an effective approach for enhancing individuals' understanding and abilities to transform their health-related behaviors through the process of acquiring knowledge. The purpose of this activity is to increase awareness of adolescent health education in Frater Don Bosco Junior High School . The methods this activity conducts interactive seminars and to assess the students' comprehension, the team conducts pre and post-tests utilizing the Web Tool platform to develop interactive quizzes. The result findings this activity can involve participants activaly and indicated a significant increase of 20.64% in participants' knowledge. The suggested recommendation for promoting enhanced understanding of adolescent health among students is to offer training programs to teachers and establish peer groups in schools.