Reviewerly/exHarmony huggingface dataset:
Reimagining Teacher Preparation: Digital Literacy in the Post-Pandemic Era

Authors:
raw_author_name: Yu-Mei Wang
raw_affiliation_string: Department of Curriculum and Instruction, School of Education, University of Alabama at Birmingham, Birmingham, Alabama 35233, United States.

Abstract: The advent of COVID-19 exposed significant flaws in the capacity of educational institutions to deliver quality instruction to their students in a crisis. The rapid instigation of advanced digital technologies to facilitate remote learning by universities and colleges provided a pedagogical bridge to cover the time and space restrictions forced on the normal face-to-face classroom experience. Unfortunately, as the impact of the pandemic has waned, educational institutions drew down their investments in the digital technology infrastructure, hardware, and software that were indispensable to sustaining student learning over the two years the pandemic interrupted normal educational schedules. However, it is argued that it would be extremely prudent to expand rather than contract these resources. Research indicates that many teachers struggled with technologies in remote teaching during COVID-19. This raises the question of how teacher education programs can adequately provide future teachers with essential digital literacy skills to teach in diverse technology-enriched learning environments. The proposed frameworks aim to reshape the curriculum in teacher education programs to cultivate and develop preservice teachers’ digital literacy skills.
Hubungan Penggunaan ChatGPT Dengan Pemenuhan Kebutuhan Mahasiswa

Authors:
raw_author_name: None Dedi Rumawan Erlandia
raw_affiliation_string: Universitas Padjadjaran

Abstract: The rise in the phenomenon of using Artificial Intelligence such as ChatGPT and the very high trust of the Indonesian people in the development of Artificial Intelligence, especially the Gen Z generation group with Higher Education status (students), has given rise to debate in terms of the impact of its uses as a technology that helps the learning process. On the positive side, ChatGPT can provide personalized and interactive learning, while on the negative side, ChatGPT can provide incorrect and inaccurate information. Meanwhile, penetration of its uses can no longer be avoided because it is easy to access, so the good and bad sides will continue to be felt, especially for its users. Moreover, until now there is still very little research on how this technology can help fulfill the needs of students within the research campus, especially the most important need for them as someone who is pursuing higher education, namely the need for satisfying learning. Therefore, this research aims to explain the relationship between the uses of ChatGPT and the fulfillment of learning needs for students at the Faculty of Communication Sciences, Padjadjaran University with the help of the Uses and Gratification theory approach. The research method usesd is a quantitative method with a type of correlational study design using the Spearman Rank correlation test The results of this study show that there is a positive and significant relationship between the uses of ChatGPT and fulfilling the learning needs of students at the Faculty of Communication Sciences, Padjadjaran University with a correlation coefficient of 0.834. So, it can be concluded that the higher the uses of ChatGPT, the higher the fulfillment of students' learning needs, which means that frequent uses of ChatGPT can indicate that it is closely related to satisfying learning needs.
The Influence of Teaching Using English Songs on Students' English Pronunciation Skills at Stikes Eka Hope Palangka Raya

Authors:
raw_author_name: Ipan Dwi Nata
raw_affiliation_string: STIKES Eka Harap Palangka Raya

Abstract: This study aims to determine the effect of teaching using English songs on the English pronunciation ability of students at STIKES EKA HARAP Palangka Raya. In this study, researchers used a pre-experimental design with one pretest and posttest group. The sample in this study was S1 Nursing students in semester 3 consisting of 20 students. Researchers gave a test using English songs. The research instruments are tests (English songs), pronunciation tests and questionnaires to collect the required data. The results of this study show that the null hypothesis is rejected because the Normal distribution table shows that the important value for the right-tail test for z in ? = .05 is z = 1.65. Since -3.92 &gt; 1.65, the researcher rejected H0: ?1 = ?2 and accepted Ha: ?1&lt;?2. Test results are statistically significant at a rate of 5%. Based on this research, it was found that the use of songs in teaching English produces a significant influence on the competence of students' English pronunciation skills and is very beneficial for students to facilitate them in learning English.
Exploring the Impact of Mobile Augmented Reality on COVID-19 Prevention Education in Primary Schools

Authors:
raw_author_name: None Syahrul Nizam Junaini
raw_affiliation_string: Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia

Abstract: This study aimed to evaluate the effectiveness of an interactive mobile Augmented Reality (AR) game to increase the knowledge about COVID-19 prevention primary school students. We tested the application for usability and effectiveness through pre- and post-tests, questionnaires, and interviews. 12 participants from four states in Malaysia took part in the study. Their average age is 9.5 years old. Results indicated a significant improvement in student performance from the pre-test to the post-test, with a mean score has increased from 3.67 to 8.25. The average System Usability Scale (SUS) score was 75%. These findings show the effectiveness of our mobile AR application as a tool to increase the knowledge about COVID-19 prevention among primary school. The findings of this study contribute to the body of research on the use of AR in COVID-19 prevention education among primary school students. This study provides both theoretical and practical implications for educators, researcher and policymakers seeking to use mobile AR to support the prevention education of any future pandemic or infectious disease.
ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

Authors:
raw_author_name: Saad Said Alqahtany
raw_affiliation_string: Faculty of Computer and Information System, Islamic University of Madinah, Madinah 42351, Saudi Arabia
raw_author_name: Toqeer Ali Syed
raw_affiliation_string: Faculty of Computer and Information System, Islamic University of Madinah, Madinah 42351, Saudi Arabia

Abstract: In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each forensic transaction into an immutable blockchain entry, establishing transparency and authenticity from data preservation to final reporting. Our framework was designed to manage a wide range of forensic applications across different domains, including technology-focused areas such as the Internet of Things (IoT) and cloud computing, as well as sector-specific fields like healthcare. Centralizing our approach are smart contracts that seamlessly connect forensic applications to the blockchain via specialized APIs. Every action within the forensic process triggers a verifiable transaction on the blockchain, enabling a comprehensive and tamper-proof case presentation in court. Performance evaluations confirmed that our system operates with minimal overhead, ensuring that the integration bolsters the judicial process without hindering forensic investigations.
Deep Learning-based Ensemble Approach for Conventional Pap Smear Image Classification

Authors:
raw_author_name: Paisit Khanarsa
raw_affiliation_string: King Mongkut's University of Technology Thonburi, Thailand

Abstract: Cervical cancer screening allows the early signs of precancerous abnormalities in the cervix before they develop into invasive cancer. The Pap Smear is a widely used screening for early detection and prevention of cervical cancer. In many remote areas, the number of cytologists available to interpret pap smear screening tests is insufficient. This lack of personnel makes the test interpretation very time-consuming. To address this, deep learning techniques have been employed to detect cervical cancer cells and support cytologists. Therefore, an integrative approach with deep learning models and the ensemble techniques such as the maximum occurrence and the maximum probability score of cervical cells was proposed. The multi-cell assessment of the Pap smear slide allowed aggregate predictions of single cervical cell images using the proposed method. The classification results between pre-trained deep learning models and the proposed method were compared. In the experimental results, the proposed method can achieve an accuracy score of more than 97%, while the best pre-trained deep learning model can attain an accuracy score of more than 85%. Hence, the proposed method may have the potential to assist physicians or cytologists in the classification of cervical cell types for Pap Smear images.
Simulation Test Research on Typical Simulator Ns-2 in Urban Vehicle Ad Hoc Network Based on Big Data

Authors:
raw_author_name: Xiaoting Wang
raw_affiliation_string: Kaifeng University, Kaifeng 475004, China Kaifeng, Henan 475004 China
raw_author_name: Zhan Zhao
raw_affiliation_string: Kaifeng University, Kaifeng 475004, China Kaifeng, Henan 475004 China

Abstract: Abstract Vehicle Ad Hoc Network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANET are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANET have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, Vehicle-to-Everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. We have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000s,14 key packets have not arrived in IEEE 802.11a and 8 packets have not arrived in IEEE 802.11p. at 8.0000s 38 key packets have not arrived in IEEE802.11a and 6 packets have not arrived in IEEE802.11p. Compare the performance of IEEE 802.11a and IEEE 802.11p.The study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.
Subjective learning as a deep postulate of the modern concept of higher (university) education

Authors:
raw_author_name: Anatoly V. Kupavtsev

Abstract: The phenomenon of subjective learning, discovered by Russian psychology and pedagogy as a higher educational technology of universities, has not, however, been actively developed and introduced into the system of higher professional education. The instinctive (instrumental) acquisition of types of activity experience is similar to the results of animal training. But a person masters all the wealth of socio-historical experience of the activities of previous generations of people, clothed in a word, thought, image. The appropriation of functional and operational mechanisms of a lifetime kind of socio-historical experience of human activity by an individual can be realized only in subjective learning in the form of individual self-study teaching actions and activities in general and cognitive self-assimilation of the conceptual provisions of science. Subjective learning will not take place in full without the student’s self-study in a scientific discipline and the active participation of the teacher as a mentor and supervisor. In the conducted exclusive pedagogical experiment, the search was carried out for the forms of the fundamentally new initiative type of educational practice being discussed — the subject-activity personalized self-learning of students to the conceptual appropriation of knowledge in dialogue with the teacher.
PENERAPAN K-MEANS DAN K-MEDOIDS BERBASIS RFM PADA SEGMENTASI PELANGGAN DI MASA PANDEMI COVID-19

Authors:
raw_author_name: Dwiza Riana
raw_affiliation_string: Universitas Nusa Mandiri
raw_author_name: Rachmawati Darma Astuti
raw_affiliation_string: Universitas Bina Sarana Informatika

Abstract: The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.
Mengukur Kualitas Layanan Perpustakaan Universitas Muslim Nusantara Al-Washliyah Medan Menggunakan Metode Libqual

Authors:
raw_author_name: Retno Sayekti
raw_affiliation_string: UIN Sumatera Utara Medan

Abstract: This research discusses measuring the quality of library services at the Muslim Nusantara Al Washliyah University of Medan using the Libqual method. The aim of the research is to determine the service quality of the Muslim Nusantara Al Washliyah Medan University Library based on three libqual dimensions, namely Affect of Service, Information Control and Library as Place. This type of research is quantitative research with a descriptive approach. The data collection technique in this research was carried out through distributing questionnaires. The sample in this study was 3,159 users of the Nusantara Al Washliyah Medan Muslim University Library from 2021, who were limited using the Slovin formula, so the number of respondents was 97 people. The sampling technique in this research used non-random accidental sampling. The results of this research show that service quality is based on the Affect of Service aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.03 and the quality of library services is considered good but has not met ideal expectations. what the user wants or is in the tolerance zone with the superiority gap (SG) being negative at a score of -1.01. Based on the Information Control aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.05 and the quality of library services is considered good but has not met the ideal expectations desired by users or is in the tolerance zone with the superiority gap (SG) being negative at a score of -1.07. Based on the Library as Place aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.16 and the quality of library services is considered good but has not met the ideal expectations desired by users or is in the zone tolerance with superiority gap (SG) is negative at a score of -1.11.
Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks

Authors:
raw_author_name: Nishant Kumar
raw_affiliation_string: Institute of Software and Multimedia Technology, Technische Universität Dresden, 01187 Dresden, Germany
raw_author_name: Sven Eckert
raw_affiliation_string: Institute of Fluid Dynamics, Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany
raw_author_name: Stefan Gumhold
raw_affiliation_string: Institute of Software and Multimedia Technology, Technische Universität Dresden, 01187 Dresden, Germany

Abstract: Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells. Additionally, these gas bubbles induce alterations in the conductivity of the electrolyte, leading to corresponding fluctuations in the magnetic flux density outside of the electrolysis cell, which can be measured by externally placed magnetic sensors. By solving the inverse problem of the Biot-Savart Law, we can estimate the conductivity distribution as well as the void fraction within the cell. In this work, we study different approaches to solve the inverse problem including Invertible Neural Networks (INNs) and Tikhonov regularization. Our experiments demonstrate that INNs are much more robust to solving the inverse problem than Tikhonov regularization when the level of noise in the magnetic flux density measurements is not known or changes over space and time.
Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

Authors:
raw_author_name: Angel M. Gama Garcia
raw_affiliation_string: School of Computing, Engineering and Physical Sciences (CEPS), University of the West of Scotland (UWS), High Street, Paisley PA1 2BE, UK
raw_author_name: Jose M. Alcaraz Calero
raw_affiliation_string: School of Computing, Engineering and Physical Sciences (CEPS), University of the West of Scotland (UWS), High Street, Paisley PA1 2BE, UK
raw_author_name: Higinio Mora Mora
raw_affiliation_string: Department of Computer Science and Technology, Universidad de Alicante, Carr. de San Vicente del Raspeig, 03690 Alicante, Spain

Abstract: In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.
A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining

Authors:
raw_author_name: Iván Durán-Díaz
raw_affiliation_string: Signal Theory and Communications Department, University of Seville, Avda. Descubrimientos S/N, 41092 Seville, Spain
raw_author_name: Auxiliadora Sarmiento
raw_affiliation_string: Signal Theory and Communications Department, University of Seville, Avda. Descubrimientos S/N, 41092 Seville, Spain
raw_author_name: Irene Fondón
raw_affiliation_string: Signal Theory and Communications Department, University of Seville, Avda. Descubrimientos S/N, 41092 Seville, Spain
raw_author_name: Clément Bodineau
raw_affiliation_string: Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115, USA
raw_author_name: Mercedes Tomé
raw_affiliation_string: Centro Andaluz de Biología Molecular y Medicina Regenerativa—CABIMER, Consejo Superior de Investigaciones Científicas, Universidad de Sevilla, Universidad Pablo de Olavide, 41092 Seville, Spain
raw_author_name: Raúl V. Durán
raw_affiliation_string: Centro Andaluz de Biología Molecular y Medicina Regenerativa—CABIMER, Consejo Superior de Investigaciones Científicas, Universidad de Sevilla, Universidad Pablo de Olavide, 41092 Seville, Spain

Abstract: Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results. However, this method was highly dependent on the parameters that control sparseness and non-negativity, as well as on algorithm initialization. Furthermore, the previously proposed method required a reference image as a starting point for the NMF algorithm. In the present work, we propose a new, simpler and more robust method for the automated, unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest) stains. The new proposed method represents a simpler approach that, on the one hand, avoids the use of NMF in the separation stage and, on the other hand, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest using principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing for color vector determination by considering the point density peaks. A new scoring stage is also developed in our method that, again, avoids reference images, making the procedure more robust and less dependent on parameters. Semi-quantitative image scoring experiments using five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.