bluuebunny/crossref_metadata_2025 huggingface dataset:
A “Parábola do (bom) samaritano” em duas versões: refletindo sobre edificação de sentidos em textos bíblicos via teoria dialógica
Authors: Gomes Graziella Steigleder , Delanoy Cláudio Primo
Published: December 2023

Temos como intento, neste artigo, iluminar facetas sobre como relações de sentido podem ser desencadeadas a partir do cotejamento de diferentes versões de um texto; neste caso, das Escrituras Sagradas. Para tanto, torna-se objeto de estudo uma parábola, gênero discursivo encontrado especialmente na esfera discursiva bíblica. Toma-se como referencial postulados advindos da teoria dialógica do discurso, igualmente referenciada como teoria bakhtiniana; são eles: dialogismo, entonação, palavra e estilo. Adicionalmente, apresentam-se métodos e tipos de tradução, embasamento que nos fornece elementos para conduzir este estudo. Como gesto de análise, verificou-se um índice de oscilação no que tange especialmente ao estilo, já que as traduções são pautadas por distintos métodos, e também por seu público leitor projetado. Assim, sentidos potencialmente distintos podem ser reverberados para os leitores de cada versão.
O papel da esfera discursiva no ensino de língua portuguesa a partir das habilidades das práticas de estudo e pesquisa
Authors: Rollsing Lucas Zambrano , Nascimento Maria Fernanda do , Perna Cristina Becker Lopes
Published: December 2023

O artigo tem por objetivos questionar se o conceito de Esfera Discursiva abordado pelo Círculo de Bakhtin coaduna com a proposta de campos de atuação da BNCC e fomentar discussões acerca da aplicabilidade do conceito no ensino de língua portuguesa como etapa inicial para a aquisição de um gênero discursivo. Analisaram as habilidades do eixo de leitura/escuta do campo de atuação das práticas de estudo e pesquisa à luz das contribuições do Círculo de Bakhtin (Bakhtin, 2003, 2006, 2016, 2017; Volóchinov, 2017, 2019) entre outros autores. Na análise das habilidades propostas, parece não haver uma distinção clara daquilo que se entende por atividades típicas de uma esfera discursiva. Pode-se verificar que esse aspecto fica um pouco de lado quando se analisa os textos descritivos das habilidades. Elas consideram os contextos de produção, contudo não focalizam seus falantes e suas atividades dentro da esfera, tomando-se assim o texto como produto pronto.
Discursos sobre avaliação: uma discussão no tocante ao Ensino Remoto Emergencial
Authors: Gonçalves Tamiris Machado , Stübe Angela Derlise
Published: December 2023

Este artigo analisa discursos sobre avaliação, especificamente nas dinâmicas de Ensino Remoto Emergencial quando da pandemia de Covid-19. O objetivo específico é entender os desafios enfrentados por professores de Língua Portuguesa da cidade de Chapecó-SC nas práticas de avaliação levadas a cabo no cenário sem interações presenciais. Trata-se de uma investigação qualitativa, cuja coleta de dados se deu em ambiente virtual, por aplicação de questionário, via Google Formulário. A análise dos dados está amparada teórico e metodologicamente na Teoria Dialógica do Discurso (Bakhtin, 2010; 2015; 2020; Volóchinov, 2017) e em estudos sobre avaliação (Zabala, 2014; Luckesi, 2008; 2013; Hoffmann, 2009; 2012). O gesto de análise indica que a avaliação ganhou contornos ainda mais complicados no cenário da pandemia, uma vez que se tensiona com instabilidades e incertezas próprias da esfera virtual.
Proposta de ensino de avaliação técnica de licitações de medicamentos baseada em estilos de aprendizagem
Authors: Munck Alice Kappel Roque , Fernandes Gerusa Cristina de Azevedo , Minateli Milene Machado
Published: December 2023

RESUMOA atuação do farmacêutico no ambiente hospitalar e em serviços de saúde exige o desenvol-vimento de diversas competências. No setor público, a aquisição de medicamentos é uma das principais atividades da gestão da assistência farmacêutica, envolvendo um conjunto complexo de exigências técnicas, legais e administrativas. Considerando a complexidade e a importância da gestão de medicamentos; a necessidade de promover o desenvolvimento de competências necessárias para a aquisição pública de medicamento; e o estilo de apren-dizagem de cada aprendiz, o presente estudo objetiva propor um método de ensino para a avaliação técnica farmacêutica de processos licitatórios para aquisição de medicamentos conforme a legislação sanitária e a segurança do paciente, baseado no estilo de aprendizagem do educando. A proposta pedagógica considerou como competência a capacidade de mobi-lizar diferentes conhecimentos, habilidades e atitudes para exercer o cuidado e solucionar problemas da prática profissional através da combinação dos domínios cognitivo, psicomotor e afetivo. Foram selecionadas como ferramentas de ensino texto escrito, podcast, infográfico, teach back e simulação e como metodologias de avaliação teste de múltipla escolha, debrie-fing, feedback e elaboração de parecer técnico. Buscou-se respeitar as habilidades inatas, os objetivos e os estilos de aprendizagem do educando, além de gerar interesse, mobilizar emoções e estimular o desenvolvimento de competências pelo residente, que serão colocadas a serviço da sociedade. Palavras-chave: assistência farmacêutica; educação em saúde;sistema de aprendizagem em saúde.
Atuação da preceptoria na residência multiprofissional no ambulatório de dor de um Hospital Universitário: um relato de experiência
Authors: Vieira Gláucia Cópio , Sarchis Anna Paula Campos , Ávila Maria Priscila Wermelinger
Published: December 2023

RESUMOIntrodução: No Brasil, observa-se uma população de difícil tratamento, são os portadores de dores crônicas. Em 2019, no Hospital Universitário de Juiz de Fora (HU-UFJF/ EBSERH) criou-se o Ambulatório Multiprofissional da Dor (AMD). Durante as avaliações dos pacien-tes, foi encontrado um número expressivo de pessoas com ideações suicidas. Objetivo: Pro-mover um processo de ensino-aprendizagem, utilizando metodologias ativas, estimulando o aperfeiçoamento profissional dos residentes no tratamento de pacientes com dor crônica e ideações suicidas. Métodos: Trata-se de um relato de experiência de um projeto de interven-ção do tipo Plano de Preceptoria que foi realizado no HU-UFJF/EBSERH. no Ambulatório Multiprofissional da Dor. Os participantes do projeto foram os Residentes Multiprofissionais em Saúde do Adulto com ênfase em doenças crônico-degenerativas, envolvendo as áreas de fisioterapia, educação física, nutrição, assistência social e psicologia. A condução do projeto ficou a cargo de duas fisioterapeutas que atuam como Preceptoras. Considerações finais: Este trabalho destaca a necessidade de qualificação na área da residência em saúde em rela-ção à dor crônica no Brasil. Conclui-se que o projeto apresentou uma abordagem abrangente e inovadora para o tratamento da dor crônica, destacando a importância da formação pro-fissional, do enfoque biopsicossocial e do cuidado com a saúde mental dos pacientes e dos profissionais envolvidos.Palavras-chave: preceptoria; dor crônica; ensino.
Ativismo judicial: mito e realidade: uma análise conceitual segundo a perspectiva de Ronald Dworkin
Authors: FONTES FELIPE NASCIMENTO
Published: December 2023

O presente trabalho possui como objetivo a análise dos fatores que fomentam o movimento que
retrata o a expansão e protagonismo do Poder Judiciário na sociedade hodierna, o qual a doutrina
convencionou chamar de “ativismo judicial”. A gênese do termo “ativismo judicial” foi a partir de um
artigo publicado em jornal americano, cuja finalidade era apenas tecer comentários a respeito das
divisões ideológicas dos membros da Suprema Corte, surgindo sem qualquer pretensão de refletir
ou inaugurar uma teoria a respeito da atuação do Poder Judiciário. Assim, apesar de surgir com uma
conotação pejorativa, o termo foi adotado pela doutrina e começaram os debates sobre a nova
postura adotada pelas Cortes e magistrados após o processo de constitucionalização. Com a
intensidade, variação e complexidade de conflitos sociais provocando frequentemente a atuação do
Poder Judiciário, há uma supervalorização desse Poder, o qual tem sido visto como instrumento
estatal de realização de garantias e pacificação social. Frente a uma Constituição de conteúdo
compromissório e as demandas sociais judicializadas, o juiz não pode se manter preso a uma
interpretação positivista, necessitando desenvolver uma intepretação do direito calcado na teoria da
integridade. Nesse contexto neoconstitucionalista e que exige do magistrado o esforço “hercúleo” no
ato de intepretação/aplicação do direito, é que este trabalho traz à tona a análise do conceito de
ativismo judicial e suas dimensões, segundo a teoria do direito como integridade do jusfilósofo
Ronald Dworkin.
Palavras-chave : Ativismo judicial. Aplicação do Direito. Constitucionalização. Judicialização.
Teoria da Integridade.
A Mini Review of Newly Identified Omicron Sublineages
Authors: Dasaradharami Reddy K , Anusha S , Palem Chandrakala
Published: December 2023

The ongoing COVID-19 pandemic has seen the evolution of the SARS-CoV-2 virus, resulting in the emergence of various concerning variants with unique biological characteristics. As the pandemic continues, it will be crucial to promptly evaluate the potential of any new variant to cause severe illness. The severity of the latest Omicron sublineages, including BA.5, XBB, BQ.1.18, BA.2, BA.2.75, and EG.5.1, is currently under assessment. This system provides valuable and essential information for rapidly assessing the threat posed by new versions of the virus.
Forensic Comparison of Textile Fibre for Identification using X-ray Diffraction Technique
Authors: MC Janaki , S Anil Kumar
Published: December 2023

This study delves into the forensic examination of textile fibers for identification through the application of the X-ray diffraction (XRD) technique. With the textile industry producing an array of materials, both natural and man-made fibers, the need to distinguish between them for forensic purposes becomes paramount. The primary objective of this research is to identify unique characteristics in fiber samples, differentiating between branded and non-branded company textiles. The focus is placed on fresh, unused cloth fibers obtained directly from shops. The study encompasses two broad categories of fibers: natural (such as cotton, silk, and wool) and man-made (including nylon, rayon, and polyester). Samples from both branded and non-branded textiles undergo analysis using XRD, a sophisticated method capable of revealing the crystallographic structure of materials. Results obtained from the XRD analysis unveil intensity peaks at various levels and degrees, providing distinctive patterns for individualization. Even within the same fiber category, such as polyester and cotton, discernible differences in intensity peaks facilitate the identification process. This research contributes to the advancement of forensic techniques by offering a reliable means of identifying textile fibers. The utilization of XRD not only allows for the differentiation between natural and man-made fibers but also enables discrimination among textiles produced by different companies. The implications of this study extend to forensic investigations, where the ability to precisely identify fibers can provide valuable evidence in criminal cases involving textiles.
Impact of COVID-19 Lockdown on Continuation of Treatment among the OPD Geriatric Patients
Authors: Das Sasmita , Nayak Priyanka Priyadarsini , Rath Rashmipriya , Nayak Suchismita
Published: December 2023

The aim of the study was to identify the impact of COVID-19 lockdown on continuation of treatment among the OPD geriatric patients. Quantitative research study has been conducted. A descriptive research design and convenience sampling technique were used to pick a total of 100 samples, (n=100). Self-structured socio demographic questionnaire and self structured interview method were used to collect the data in November 2020 at tertiary care hospitals, Bhubaneswar, Odisha, India. The collected data were analyzed through descriptive and inferential statistics. Comparison of three categories of COVID-19 pandemic factors affecting in availing OPD services reveals that the psychological factor has more effect (73%) followed by hospital related factors (68%) and the availability of public transportation and resources (62%) has less effect in availing OPD services among patients during COVID-19 pandemic. Application of chi square test reveals that there is no significant relationship of intensity of COVID-19 pandemic factors affecting in availing OPD services among patients with selected socio demographic variables. These findings suggest that facility for sanitization of hospital premises, facility for social distancing in registration counter, waiting area and other areas of hospital are not quite satisfactory. Factors like availability of public transport, availability of healthcare personnel’s in OPD, adequacy of treatment facility, cost of medical services, availability of diagnostic services, are highly affecting geriatric patients in availing OPD services during COVID-19 pandemic and need to be taken care.
The Effect of Inquiry-Based Learning Strategy on EFL Tenth-Grade Students’ Reading Comprehension
Authors: Al-Khamaiseh Hadeel Saleh
Published: December 2023

This study examined the potential effect of Inquiry-Based Learning Strategy (IBL) on the tenth-grade students' reading comprehension. Two groups and a quasi-experimental design were used. Two complete sections of grade 10 students from a public Secondary School for Girls in Irbid was randomly assigned by the researcher. The experimental group of 30 students was chosen first, and then the control group of 30 students. A pre-post reading comprehension test was designed before and after the study in order to fulfill its goals. Additionally, the experimental group was taught using the IBL strategy, whereas the control group was taught using the traditional teaching methods recommended in the tenth-grade Teacher's Book. According to the findings, there were significant statistical differences favoring the experimental group over the control group. In light of the findings, the researcher recommended employing the IBL strategy to students with various levels and EFL skills.
Test Case Prioritization for Software Product Line: A Systematic Mapping Study
Authors: Idham Muhammad , Halim Shahliza Abd , Jawawi Dayang Norhayati Abang , Zakaria Zalmiyah , Erianda Aldo , Arss Nachnoer
Published: December 2023

Combinatorial explosion remains a common issue in testing. Due to the vast number of product variants, the number of test cases required for comprehensive coverage has significantly increased. One of the techniques to efficiently tackle this problem is prioritizing the test suites using a regression testing method. However, there is a lack of comprehensive reviews focusing on test case prioritization in SPLs. To address this research gap, this paper proposed a systematic mapping study to observe the extent of test case prioritization usage in Software Product Line Testing. The study aims to classify various aspects of SPL-TCP (Software Product Line â€“ Test Case Prioritization), including methods, criteria, measurements, constraints, empirical studies, and domains. Over the last ten years, a thorough investigation uncovered twenty-four primary studies, consisting of 12 journal articles and 12 conference papers, all related to Test Case Prioritization for SPLs. This systematic mapping study presents a comprehensive classification of the different approaches to test case prioritization for Software Product Lines. This classification can be valuable in identifying the most suitable strategies to address specific challenges and serves as a guide for future research works. In conclusion, this mapping study systematically classifies different approaches to test case prioritization in Software Product Lines. The results of this study can serve as a valuable resource for addressing challenges in SPL testing and provide insights for future research.
Classification of Sugarcane Area Using Landsat 8 and Random Forest based on Phenology Knowledge
Authors: Sudianto Sudianto , Herdiyeni Yeni , Prasetyo Lilik Budi
Published: December 2023

Indonesia is one of the largest countries globally with an area for planting sugarcane. The current problem is that determining the planting area of sugarcane is still done conventionally; this is very limited and wastes time. Thus, knowing the sugarcane planting area becomes essential for policymaking through Remote Sensing technology. However, the challenge of Remote Sensing is the limited data due to weather and the spectral variability of other plants. So, it is necessary to classify based on phenological knowledge. The study aims to classify sugarcane areas based on phenological knowledge using Remote Sensing and Machine Learning. This application finished on the cloud platform Google Earth Engine (GEE) through Landsat 8 satellite imagery data. The knowledge of sugarcane phenology was built based on the Normalized Difference Vegetation Index (NDVI) spectral value and built with the harmonic model. In addition, classification is accomplished by object-oriented (OO) methods for segmentation classification. Object-oriented is solved by the Simple Non-Iterative Clustering (SNIC) algorithm for spatial cluster identification, the Gray-Level Co-occurrence Matrix (GLCM) for texture extraction, and the Random Forest algorithm for Land Use-Land Cover (LULC) classification. The results of the accuracy analysis using the confusion matrix and the classification of sugar cane areas based on phenological knowledge obtained the best results with an overall accuracy of 95.9% with a Kappa coefficient of 0.92. It can be concluded that a classification approach with knowledge of plant phenology can help better classify the availability of land for plantations in the future.
Automatic Topic-Based Web Page Classification Using Deep Learning
Authors: Apandi Siti Hawa , Sallim Jamaludin , Mohamed Rozlina , Ahmad Norkhairi
Published: December 2023

The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.
No-Show Passenger Prediction for Flights
Authors: Chin Wei-Song , Ting Choo-Yee , Cham Chin-Leei
Published: December 2023

In aviation, â€œno-showâ€ refers to a customer who booked a reservation but failed to show up. No-shows can result in various resource wastes, such as vacant seats, leading to income loss and flight delays. As a result, no-show passengers can cause considerable problems for airlines, ultimately affecting their bottom line. Recent research has shown the use of machine learning algorithmsÂ to reduce the rate of no-shows. For example, a researcher in healthcare is using a predictive model to identify no-showsâ€™ patients to increase efficiency.Â Therefore, this study aimed to develop prediction models to predict passenger no-shows. In this work, we used a dataset supplied by a local airline company consisting of 1,046,486 rows and 8 columns. Additional datasets like weather data, public holiday data of different countries, aircraft details, and foot traffic data are used to carry out the dataset's feature enrichment task to complement the original dataset. As a result, feature selection has become an important stage in this research to identify and pick the most relevant and useful features from the enormous number of columns. The findings showed that the model built using Random Forest has the highest accuracy of 90.4%, while Decision Tree performed at 90.2%, Gradient Boosting at 86.5%, and Neural Networks at 67.6%.Â To enhance the accuracy of the models, further research efforts are essential to integrate supplementary passenger information.
Practical Evaluation of Federated Learning in Edge AI for IoT
Authors: Pal Sauryadeep , Umair Muhammad , Tan Wooi-Haw , Foo Yee-Loo
Published: December 2023

AI running locally on IoT Edge devices is called Edge AI. Federated Learning (FL) is a Machine Learning (ML) technique that builds upon the concept of distributed computing and preserves data privacy while still supporting trainable AI models. This paper evaluates the FL regarding practical CPU usage and training time. Additionally, the paper presents how biased IoT Edge clients affect the performance of an AI model. Existing literature on the performance of FL indicates that it is sensitive to imbalanced data distributions and does not easily converge in the presence of heterogeneous data. Furthermore, model training uses significant on-device resources, and low-power IoT devices cannot train complex ML models. This paper investigates optimal training parameters to make FL more performant and researches the use of model compression to make FL more accessible to IoT Edge devices. First, a flexible test environment is created that can emulate clients with biased data samples. Each compressed version of the ML model is used for FL. Evaluation is done regarding resources used and the overall ML model performance. Our current study shows an accuracy improvement of 1.16% from modifying training parameters, but a balance is needed to prevent overfitting. Model compression can reduce resource usage by 5.42% but tends to accelerate overfitting and increase model loss by 9.35%.
TPPSO: A Novel Two-Phase Particle Swarm Optimization
Authors: Shami Tareq M. , Summakieh Mhd Amen , Alswaitti Mohammed , Jahdhami Majan Abdullah Al , Sheikh Abdul Manan , El-Saleh Ayman A.
Published: December 2023

Particle swarm optimization (PSO) is a stout and rapid searching algorithm that has been used in various applications. Nevertheless, its major drawback is the stagnation problem that arises in the later phases of the search process. To solve this problem, a proper balance between investigation and manipulation throughout the search process should be maintained. This article proposes a new PSO variant named two-phases PSO (TPPSO). The concept of TPPSO is to split the search process into two phases. The first phase performs the original PSO operations with linearly decreasing inertia weight, and its objective is to focus on exploration. The second phase focuses on exploitation by generating two random positions in each iteration that are close to the global best position. The two generated positions are compared with the global best position sequentially. If a generated position performs better than the global best position, then it replaces the global best position. To prove the effectiveness of the proposed algorithm, sixteen popular unimodal, multimodal, shifted, and rotated benchmarking functions have been used to compare its performance with other existing well-known PSO variants and non-PSO algorithms. Simulation results show that TPPSO outperforms the other modified and hybrid PSO variants regarding solution quality, convergence speed, and robustness. The convergence speed of TPPSO is extremely fast, making it a suitable optimizer for real-world optimization problems.
EmoStory: Emotion Prediction and Mapping in Narrative Stories
Authors: Too Seng-Wei , See John , Quek Albert , Goh Hui-Ngo
Published: December 2023

A well-designed story is built upon a sequence of plots and events. Each event has its purpose in piquing the audience's interest in the plot; thus, understanding the flow of emotions within the story is vital to its success. A story is usually built up through dramatic changes in emotion and mood to create resonance with the audience. The lack of research in this understudied field warrants exploring several aspects of the emotional analysis of stories. In this paper, we propose an encoder-decoder framework to perform sentence-level emotion recognition of narrative stories on both dimensional and categorical aspects, achieving MAE=0.0846 and 54% accuracy (8-class), respectively, on the EmoTales dataset and a reasonably good level of generalization to an untrained dataset. The first use of attention and multi-head attention mechanisms for emotion representation mapping (ERM) yields state-of-the-art performance in certain settings. We further present the preliminary idea of EmoStory, a concept that seamlessly predicts both dimensional and categorical space in an efficient manner, made possible with ERM. This methodology is useful in only one of the two aspects is available. In the future, these techniques could be extended to model the personality or emotional state of characters in stories, which could benefit the affective assessment of experiences and the creation of emotive avatars and virtual worlds
Neural Network Based Data Encryption: A Comparison Study among DES, AES, and HE Techniques
Authors: Yeow Sin-Qian , Ng Kok-Why
Published: December 2023

With the improvement of technology and the continuous expansion and deepening of neural network technology, its application in computer network security plays an important role. However, the development of neural networks is accompanied by new threats and challenges. This paper proposes to encrypt the weight data using encryption algorithms and embed image encryption algorithms to improve protected data security further. The purpose is to address the feasibility and effectiveness of using modern encryption algorithms for data encryption in machine learning in response to data privacy breaches. The approach consists of training a neural network to simulate a model of machine learning and then encrypting it using Data Encryption Standard (DES), Advanced Encryption Standard (AES), and Homomorphic Encryption (HE) techniques, respectively. Its performance is evaluated based on the encryption/decryption accuracy and computational efficiency. The results indicate that combining DES with Blowfish offers moderate encryption and decryption speeds but is less secure than AES and HE. AES provides a practical solution, balancing security and performance, offering a relatively swift encryption and decryption process while maintaining high security. However, Fernet and HE present a viable alternative if data privacy is a top priority. Encryption and decryption times increase with file size and require sufficient computational resources. Future research should explore image encryption techniques to balance security and accurate image retrieval during decryption. Advanced privacy-preserving approaches, such as differential privacy and secure multi-party computation, may enhance security and confidentiality in digital encryption and decryption processes.
Predicting Factors that Affect East Asian Studentsâ€™ Reading Proficiency in PISA
Authors: Low Adeline Hui-Min , Lim Amy Hui-Lan , Chua Fang-Fang
Published: December 2023

Teachers, schools, and parents contribute to equipping students with essential knowledge and skills during their education years. When students are approaching the end of their education, they are randomly selected to participate in Program for International Student Assessment (PISA) to assess their reading proficiency. Existing work on analyzing PISA achievement results concentrates solely on identifying factors related to Parent or in combination with Student. Limited work has been proposed on how factors related to Teacher and School affect the studentsâ€™ reading proficiency in PISA. This study focuses on identifying the factors related to Teacher and/or School that affect East Asian studentsâ€™ reading proficiency in PISA. The PISA achievement results from East Asian students are chosen as the domain study because they are consistently the top performers in PISA in the past decade. Decision Tree (DT), NaÃ¯ve Bayes (NB), K-Nearest Neighbors (KNN) and Random Forest (RF) are compared. Hamming score is used as the evaluation metric. The results indicate that RF produces the best predictive models with highest Hamming score of 0.8427. Based on the findings, School-related factors such as the number of schoolâ€™s disciplinary cases, size of the school, the availability of computers with Internet facilities, the quality and educational qualifications of teachers have higher impact on the PISA achievement results. The identified factors can be used as a reference in assessing the current schoolâ€™s teaching, learning environment, and organizing extra activities as part of intervention programs to cultivate reading habits and enhance reading abilities among students.
Evaluation of Cryptocurrency Price Prediction Using LSTM and CNNs Models
Authors: Wen Ng Shi , Ling Lew Sook
Published: December 2023

Cryptocurrencies created by Nakamoto in 2009 have gained significant interest due to their potential for high returns. However, the cryptocurrency market's unpredictability makes it challenging to forecast prices accurately. To tackle this issue, a deep learning model has been developed that utilizes Long Short-Term Memory (LSTM) neural networks and Convolutional Neural Networks (CNNs) to predict cryptocurrency prices. LSTMs, a type of recurrent neural network, are well-suited for analyzing time series data and have been successful in various prediction applications. Additionally, CNNs, primarily used for image analysis tasks, can be employed to extract relevant patterns and characteristics from input data in Bitcoin price prediction applications. This study contributes to the existing related works on cryptocurrency price prediction by exploring various predictive models and techniques, which involve a machine learning model, deep learning model, time series analysis, and as well as a hybrid model that combines deep learning methods to predict cryptocurrency prices as well as enhance the accuracy and reliability of the price predictions. To ensure accurate predictions in this study, a trustworthy dataset from investing.com was sought. The dataset, sourced from investing.com, consists of 1826 time series data samples. The dataset covers the time frame from January 1, 2018, to December 31, 2022, providing data for a period of 5 years. Subsequently, pre-processing was conducted on the dataset to guarantee the quality of the input. As a result of absent values and concerns regarding the dataset's obsolescence, an alternative dataset was sourced to avoid these issues. The performance of the LSTM and CNN models was evaluated using root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE) and R-squared (R2). It was observed that they outperformed each other to a certain degree in short-term forecasts compared to long-term predictions, where the R2Â values for LSTM range from 0.973 to 0.986, while for CNNs, they range from 0.972 to 0.988 for 1 day, 3 days and 7 days windows length. Nevertheless, the LSTM model demonstrated the most favorable performance with the lowest error rate. The RMSE values for the LSTM model ranged from 1203.97 to 1645.36, whereas the RMSE values for the CNNs model ranged from 1107.77 to 1670.93. As a result, the LSTM model exhibited a lower error rate in RMSE and achieved the highest accuracy in R2Â compared to the CNNs model. Considering these comparative outcomes, the LSTM model can be deemed as the most suitable model for this specific case
In-Air Hand Gesture Signature Recognition Using Multi-Scale Convolutional Neural Networks
Authors: Chuen Alvin Lim Fang , Wee How Khoh , Han Pang Ying , Yen Yap Hui
Published: December 2023

The hand signature is a unique handwritten name or symbol that serves as a proof of identity. Due to its practicality and widespread use, hand signature is still used by financial institutions as a means of verifying and validating the identity of their customers. The emergence of the COVID-19 global pandemic has raised hygiene concerns regarding the conventional touch-based hand signature recognition system, which often requires sharing the acquisition devices among the public. This paper presents in-air hand gesture signature recognition using convolutional neural networks to address this concern. We designed a shallow multi-scale convolutional neural network using 3x3 and 5x5 kernel filter sizes to extract features on different scales. The feature maps from these two filters are then concatenated to provide more robust features, which improve the modelâ€™s performance.Â The experiment results show that the proposed architecture outperforms other architectures, which obtained the highest accuracy of 93.00%. On the other hand, our architecture consumed significantly fewer computational resources, requiring only an average of 3 minutes and 33 seconds to train. Additionally, the performance of the proposed architecture could be further enhanced by integrating it with recurrent neural networks (RNN). This integrated architecture of convolutional recurrent neural networks (C-RNN) can capture spatio-temporal features simultaneously.
An Effective Open ERP System for Automation in Financial Reporting for SMEs based on Service Oriented Architecture
Authors: Saputra Muhardi , Fadlila Rafa Fadlila
Published: December 2023

Small-Medium Enterprises (SMEs) are currently in high demand and highly developed in many countries. However, several SMEs still do not yet have an information system to support their business processes. This causes the absence of a system that helps integrate data from each process and sector in SMEs. All data exchanges and transaction report generation are done manually and recorded using physical documents that reduce effectiveness and increase costs. This research uses the accounting module in Open ERP or Odoo Version 11.0 software and Service Oriented Architecture (SOA) methods to design ERP systems for general types of SMEs in the finance sector, specifically on the sales and purchase process. So the solution for recording financial transactions and making financial reports for SMEs is to design an ERP system based on Open Source using the Service Oriented Architecture methodology, namely Smart SMEs, by implementing the Automatic Reporting feature to create best practices on an integrated system, especially in recording transactions in the sales and purchase processes so that financial reports can be generated automatically and are real-time. The result of this research is the design of ERP systems in the finance section connected occurs especially in the financial recording of the purchase of goods, the sale of goods, and the creation of financial statements automatically. This system can help the finance sector record transactions and make automatic financial reports that can be generated in real-time.
Measurement on University Websites: A Perspective of Effectiveness
Authors: Palacios-Zamora Kerly , Cordova-Morana Jorge , Mendoza-Cabrera Denis , Pacheco-Mendoza Silvia
Published: December 2023

This paper highlights the importance of evaluating the performance of university websites and how this can affect the reputation of universities. Different quality evaluation models are analyzed and emphasized in the context of efficiency and how factors such as response time, processing capacity, efficient use of resources, scalability, data transfer rate, concurrency capacity, and fault tolerance can positively or negatively affect websites. In addition, the importance of applying specific techniques to increase efficiency in loading speed is pointed out, such as image optimization, responsiveness on desktop and mobile devices, and content caching, among others, which allow to improve the website's efficiency. To conduct this process, a case study was applied where the university websites were selected, efficiency metrics were defined, and the data provided by the performance measurement tools that provide metrics and quantitative data for the evaluation were collected and analyzed. from the website. The results of the study revealed that there is room for improvement in page load time and page size optimization. In addition, the need to upgrade the performance of mobile devices was identified, given the increasing use of smartphones and tablets to access websites. As a final recommendation, it is advised to implement a comprehensive strategy to improve website performance. This strategy should include optimization of page load time and page size as well as user experience considerations. By achieving optimal performance, universities can offer their users a more satisfying online experience, thus strengthening their reputation and their ability to attract new users.
Value Clarification Technique sebagai Strategi Penyadaran Diri Mahasiswa untuk Tidak Menggosip dan Bijak dalam Gunakan Jejaring Sosial
Authors: Harmianto Sri
Published: December 2023

Pembelajaran VCT Value Clarification Technique (Teknik Mengklarifikasi Nilai) memiliki keunggulan untuk mengungkap nilai diri yang telah dimiliki mahasiswa dan dapat untuk menanamkan karakter jujur, bersahabat/komunikatif dan tanggungjawab serta nilai-nilai yang sesuai dengan tatanan norma agama. Pembelajaran VCT ini dilaksanakan dengan memberikan cerita kasus selanjutnya mahasiswa diminta mengisi matrik yang berisikan butir-butir pernyataan yang kontroversial terkait kebiasaan mahasiswa menggosip dari mulut ke mulut maupun melalui jejaring sosial. Dengan menggunakan higher question digali dan diklarifikasi jawaban mahasiswa tersebut, sekaligus ditanamkan karakter dan nilai yang baku.
PEMBELAJARAN MENULIS PUISI DENGAN MEMANFAATKAN TEKNIK BRAINWRITING PADA PESERTA DIDIK SD/MI KELAS V
Authors: Azizah Aida
Published: December 2023

Peserta didik Sekolah Dasar/Madrasah Ibtidaiyahkelas Vdalam kegiatan proses pembelajaran bahasa Indonesia khususnya dalam aspek menulis puisi masih belum maksimal. Hambatan dalam pembelajaran menulis puisi disebabkan oleh beberapa hal, antara lain: kurangnya minat peserta didik terhadap pembelajaran menulis puisi, hal itu disebabkan karena guru belum menggunakan teknik pembelajaran yang inovatif ketika pembelajaran berlangsung. Oleh karenaÂ  itu, dibutuhkan suatu teknik pembelajaran yang efektif agar mempermudah peserta didik dalam mempraktikkan menulis puisi, teknik yang digunakan yaitu teknik Brainwriting.
ALAT PERAGA KOTAK BELAJAR AJAIB (KOBELA) DALAM PEMBELAJARAN MATEMATIKA MATERI PERKALIAN DAN PEMBAGIAN SEKOLAH DASAR
Authors: Harnanto Sugeng
Published: December 2023

KOBELA adalah akronim dari Kotak Belajar Ajaib, yaitu Â alat peraga pelajaran yang berupa Â kotak kecilÂ  yang di gunakan untuk belajar (memperoleh ilmu) yang dianggap sebagai barang baru atau aneh yang di dalamnya ada alat yang di gerakanÂ  oleh manusia. Latar belakang alat peraga Â ini dibuat adalah fakta di lapangan bahwa, anak banyak yang stres mengingat fakta perkalian dan pembagian sehingga anak sudah sedih dulu sebelum belajar. Tujuan dibuatnya alat ini : (1) Meningkatkan daya konsentrasi/ perhatian siswa, (2) Meningkatkan kreaktifitas siswa, (3) Meningkatkan hasil belajar Â siswa, dan (4) Menyenangkan siswa. Apa. Kobela ini terdiri dari kotak, alat, dan kartu soal. Kotak dan alat dirangkai menjadi satu bagian, sedang kartu soal sebagai meteri pengayaan atau perbaikan. Alat ini telah diuji cobakan dalam pembelajaran kelas rendah dan kegiatan pembelajaran yang ektrakurikuler. Keberhasilan siswa dalam pembelajaran sebelum penggunaaan alat rata-rataÂ  54,56 Â (56.77%), tetapi setelah menggunakan alat tingkat keberhasilan mencapai rata-rata 90.52 (94.19 %). Tingkat ketuntasan belajar untuk Kompetensi Dasar :Â  3.1 Melakukan perkalian bilangan dua, hasilnya ada kenaikan yaitu 37,42. Dalam menggunakan Kobela ini anak didik karakter yaitu mandiri, tanggung jawab, demokratis, kreatif, aktif, cakap, jujur, berani, berilmu, dan senang. Saran yang dapat kami berikan adalah proses pembelajaran gunakan alat peraga baik itu alam sekitar atau buatan sendiri supaya anak dapat melihat, mencoba, menanya, mengomunikasikan, dan mengalami sendiri. Sebab pembelajaran akan bermakna jika anak mengalami sendiri apa yang dipelajarinya.
PEMBERDAYAAN POTENSI KEBUN POLYBAG SEBAGAI MEDIA PEMBELAJARAN TEMATIK MELALUI MODEL OUTDOOR STUDY UNTUK SISWA KELAS IV SDN 01 SUKOHARJO PATI
Authors: Ratnasari Yuni
Published: December 2023

Penelitian ini bertujuan untuk 1) Membuat kebun polybag sebagai media pembelajaran tematik melalui model outdoor study di halaman sekolah dasar, 2) Mengimplementasikan pembelajaran tematik dengan media kebun polybag melalui model outdoor study, 3) Menganalisis tingkat keefektifan dari implementasi pembelajaran tematik dengan media kebun polybag melalui model outdoor study. Pembelajaran tematik merupakan pembelajaran terpadu yang merupakan suatu sistem pembelajaran yang memungkinkan siswa, baik secara individual maupun kelompok aktif menggali dan menemukan konsep serta prinsip-prinsip keilmuan secara holistik, bermakna, dan autentik. Pemberdayaan kebun polybag adalah membudidayakan tanaman di media polybag di halaman sempit sekitar kita. Outdoor study dibatasi pada kegiatan siswa diluar kelas, mengetahui alat dan bahan untuk membuat media polybag, siswa diajari cara membuat media polybag, cara menanam sayuran, cara merawat, mengetahui perkembangan tanaman, mengenali bagian tumbuhan dan fungsinya sampai pada cara memanen tanaman Penelitian ini menggunakan metode Research and development dalam pembelajaran adalah proses yang digunakan untuk mengembangkan dan memvalidasi produk-produk yang digunakan dalam proses pembelajaran. Penelitian ini dilaksanakan pada bulan Februaru sampai Juni 2015. Subyek penelitian adalah siswa kelas IV SDN Sukoharjo 01 Margorejo Pati. Tehnik pengumpulan data dengan metode observasi, angket, dokumentasi dan tes. Â Keberhasilan penelitian ini terlihat dari Pemberdayaan kebun polybag sebagai media pembelajaran dapat dibuat dengan baik yang terdiri dari berbagai sayuran tomat, cabe, brokoli, terung dan bayam. Hasil belajar awal siswa dengan ketuntasan klasikal 35,71% meningkat menjadi 71,43% dan akhir menjadi 92,86%. Dimana jumlah siswa yang awalnya ada 9 siswa tidak tuntas menurun tinggal 1 siswa yang tidak tuntas80% dengan nilai rata-rata 74,6. Hasil tersebut membuktikan bahwa model pembelajaran outdoor study dengan memberdayakan potensi kebun polybag sebagai media tematik sangat efektif.
INTERPRETATION AND INTERNALIZATION OF MODERATION VALUES IN PONDOK MODERN DARUSSALAM GONTOR
Authors: Anam Khoirul , Kadir Abdul , Rofiq Aunur
Published: December 2023

Abstrak: Artikel ini menyajikan hasil penelitian tentang nilai-nilai moderasi (wasthiyyah) di Pondok Modern Darussalam Gontor. Penelitian ini menggunakan metode deskriptif kualitatif untuk mesdeskripsikan interpretasi dan internalisasi nilai-nilai moderasi dalam pendidikan Pondok Modern Darussalam Gontor. Data dikumpulkan dengan metode wawancara, obserasi dan dokumentasi. Hasil penelitian menyimpulkan bahwa nilai-nilai moderasi di Pondok Modern Darussalam Gontor diinterpretasikan sebagai bagian integral dari Panca Jiwa Pondok Modern Darussalam Gontor yang meliputi: jiwa keikhlasan, jiwa kesederhanaan, jiwa kemandirian, jiwa ukhuwwah Islamiyyah dan jiwa kebebasan. Nilai-nilai moderasi dalam panca jiwa tersebut diinternalisasikan secara komprehensif dalam setiap nafas, gerak dan langkah pada setiap aktifitas dan kegiatan yang mendidik melalui proses pendidikan yang berkelanjutan dengan kurikulum yang independen, terstruktur dan tersistematis melalui pola dan gaya hidup berdisiplin yang khas.

Kata Kunci : Nilai, Moderasi, Internalisasi, Gonto

Abstract: This article presents research results on moderation (wasthiyyah) values at Pondok Modern Darussalam Gontor. This study uses a qualitative descriptive method to describe the interpretation and internalization of the values   of moderation in Pondok Modern Darussalam Gontor education. Data were collected by interview, observation, and documentation methods. The results of the study concluded that the values   of moderation in Pondok Modern Darussalam Gontor are interpreted as an integral part of the Five Souls of Pondok Modern Darussalam Gontor which include: a soul of sincerity, a spirit of simplicity, a spirit of independence, a spirit of Islamic brotherhood and a spirit of freedom. The values of moderation in the five souls are comprehensively internalized in every breath, movement and step in every educational activity and practice through a continuous educational process with an independent, structured and systematized curriculum through typical disciplined patterns and lifestyles.

Keywords: Value, Moderation, Internalization, Gontor
TOWARD RELIGIOUS MODERATION: Mohammed Arkoun’s and Hasan Hanafi’s Perspectives on Indonesian Wasatiyah Islam
Authors: Muhtar Fathurrahman
Published: December 2023

Abstract : This research examines the views of Mohammed Arkoun and Hasan Hanafi on Wasatiyah Islam in Indonesia. Both Muslim scholars emphasize the importance of interfaith dialogue and the wise application of religion. This research uses a literature review to collect data from the works of Arkoun, Hanafi, and other relevant sources on Islam and Wasatiyah in Indonesia. The results showed that Arkoun and Hanafi had a significant influence on Wasatiyah Islam in Indonesia. Wasatiyah Islam encourages interfaith harmony and religious diversity through dialog and inclusiveness. The research findings imply that the ideas of Arkoun and Hanafi are important for Wasatiyah Islam in Indonesia and can help develop a moderate and inclusive outlook.

Key words : Indonesia, Islam, Wasatiyah,  interfaith harmony,  religious diversity

Abstrak : Penelitian ini mengkaji pandangan Mohammed Arkoun dan Hasan Hanafi mengenai Islam Wasatiyah di Indonesia. Kedua sarjana Muslim ini menekankan pentingnya dialog antaragama dan penerapan agama secara bijaksana. Penelitian ini menggunakan tinjauan literatur untuk mengumpulkan data dari karya-karya Arkoun, Hanafi, dan sumber-sumber lain yang relevan tentang Islam dan Wasatiyah di Indonesia. Hasil penelitian menunjukkan bahwa Arkoun dan Hanafi memiliki pengaruh yang signifikan pada Islam Wasatiyah di Indonesia. Islam Wasatiyah mendorong harmoni antaragama dan keberagaman keagamaan melalui dialog dan inklusivitas. Temuan penelitian menyiratkan bahwa gagasan Arkoun dan Hanafi penting bagi Islam Wasatiyah di Indonesia dan dapat membantu mengembangkan pandangan yang moderat dan inklusif.

Kata kunci : Indonesia, Islam, Wasatiyah, kerukunan antaragama, keberagaman agama