Reviewerly/exHarmony huggingface dataset:
Digital Competence of Primary School Design and Technology Teachers for Online Learning and Facilitation: A Pilot Study

This pilot study aimed to examine the reliability of the DIGIGLO Digital Competency Assessment instrument by using Cronbach's Alpha as a measurement of its reliability. The study involved 30 primary school Design and Technology teachers who held the position of Head of the Panel in the Batu Pahat district. Respondents answered an online questionnaire using Google Forms to provide the necessary data for this pilot study. The Statistical Package for Social Science (SPSS) software was used to analyze the data and obtain Cronbach's Alpha values. The analysis results showed that the Cronbach's Alpha values for the eight elements of digital competency ranged from .919 to .966. Therefore, the adapted DIGIGLO instrument is a valid and reliable tool for assessing teacher digital competencies.
PENGEMBANGAN RPP MODEL TGT BERBASIS PERMAINAN JAMURAN MATERI KALOR DAN PERPINDAHAN KELAS VII

Abstract: This development research is entitled Development of a Learning Implementation Plan (RPP) for the TGT Type (Team Games Tournament) Cooperative Model based on the traditional game of Mushroom on Heat and Displacement material. The background of this development research is the problem of the learning process using scientific methods but not yet in accordance with the steps and implementation. This study aims to describe the steps for developing a Learning Implementation Plan (RPP) for the Cooperative Type TGT model based on the Mushroom game. As well as describe the quality of the results of the development of the Learning Implementation Plan (RPP). The quality of the development results is determined based on the criteria of validity. The material used in the development is the material of Heat and displacement class VII.This research is research and development. The Learning Implementation Plan (RPP) development model was adapted from the Ploomp model development model which consisted of preliminary investigation, design, realization, evaluation and revision, and implementation. The research instrument used was a validation sheet for the product developed in the form of a LearningImplementation Plan (RPP). Validity data analysis was carried out by converting quantitative data in the form of assessment scores into qualitative data.This development research resulted in a Learning Implementation Plan (RPP), Student Worksheet (LKPD) and Assessment Instruments on Heat and Displacement material. Each Aspect of the Learning Implementation Plan (RPP) meets valid quality.&#x0D; Keywords: Development, RPP, TGT (Team Games Tournament), Heat a Displacemen
Federated Learning for Collaborative Network Security in Decentralized Environments

In decentralized network environments, collaborative efforts are crucial to bolstering network security against everevolving threats from malicious actors. Federated Learning has emerged as a promising solution, enabling multiple nodes to collectively train machine learning models while preserving data privacy. This research proposes SentinelNet, a novel Federated Learning framework specifically designed for collaborative network security. The framework emphasizes secure threat intelligence sharing, privacy-preserving techniques, and adaptive learning mechanisms. Through comprehensive evaluations and real-world case studies, SentinelNet demonstrates its efficacy in enhancing network security while maintaining data confidentiality. The research highlights the significance of collaborative approaches and advocates the adoption of Federated Learning to fortify decentralized network ecosystems.
Conflating SCRUM with micro-Project Based Learning. An HMU Application in an IoT Device Development

Project Based Learning has been well established in the scientific literature as a useful pedagogy tool. The same is true for SCRUM framework with respect to Industry and Education as well. Recent studies have revealed the potential of applying SCRUM methodology in a PjBL setting with very promising pedagogical results. In this work we provide further support for this active area of research by applying SCRUM in a micro-PjBL setting. It involves a small group of Erasmus Students assigned to develop a meteorological station in a short timeframe using SCRUM but, in this case, applied in a step-by-step approach (micro). The outcome of this quasi-experiment supports the effectiveness of SCRUM once more as an indispensable pedagogy tool for Active Learning purposes.
ANALISIS TINGKAT KERENTANAN BENCANA (STUDI KASUS: KAWASAN CAGAR BUDAYA CANDI MUARA JAMBI)

Cagar Budaya Candi Muara Jambi Area is one of the national heritages which is a cultural heritage in Jambi Province with an area of 3,981 hectares. The located of Kawasan Cagar Budaya Candi Muara Jambi is at the estuary of the Batanghari River which poses a threat to the flood disaster of the Batanghari River in every rainy season. The study aims to maaping the level of vulnerability and to analyse the level of disaster vulnerability by reducing the impact of losses and the impact of disaster due to the Batanghari River flood disaster in the Kawasan Cagara Budaya Candi Muara Jambi. Determination of the level of disaster vulnerability through four parameters of vulnerability, they are social vulnerability, economic vulnerability, physical vulnerability, and environmental vulnerability. The approach of the studi is GIS approach with an analysis of the level disaster vulnerability is using overlay, scoring, and weighting techniques. The results of the study is disaster vulnerability level of flood disaster in Kawasan Cagar Budaya Candi Muara Jambi is at medium level of disaster vulnerability.&#x0D; Keywords: flood disaster; watersheds; vulnerability disaster.
An interpretable approach for trustworthy intrusion detection systems against evasion samples

In recent years, Deep Neural Networks (DNN) have demonstrated remarkable success in various domains, including Intrusion Detection Systems (IDS). The ability of DNN to learn complex patterns from large datasets has significantly improved IDS performance, leading to more accurate and efficient threat detection. Despite their effectiveness, DNN models exhibit vulnerabilities to adversarial attacks, where malicious inputs are specifically crafted to deceive the models and evade detection. This paper provides insights into the effectiveness of deep learning-based IDS (DL-IDS) against adversarial example (AE) attacks. We tackle the weaknesses of DNN in detecting adversarial attacks by proposing the Convolutional Neural Network (CNN), which serves as an AE detector. We also utilize one of the XAI techniques, specifically SHAP, to enhance the transparency of the AE detector. Our results show that the AE detector has obvious effects for detecting adversarial examples and achieves an impressive 99.46% accuracy in our experimental environment.
PENGEMBANGAN MEDIA PEMBELAJARAN MATERI SISTEM KOORDINAT BERBASIS GEOGEBRA DI SEKOLAH DASAR

This study aims to develop coordinate system learning media in the form of GeoGebra Applets for fifth grade elementary school students. Learning media was developed using the Research and Development (R&amp;D) with the 4D model (Define, Design, Develop, and Disseminate). From the results of validation tests carried out by 3 experts, the results of the first validation obtained a percentage of 77.75% (valid). After revisions/improvements were carried out, the results of the second validation obtained a percentage of 93.65% (very valid). From the results of the one-by-one trial, students gave positive responses when learning the coordinate system using the GeoGebra Applet that had been developed. The results of the practicality test carried out with 3 teachers and 10 students obtained a percentage of 83.42% (very practical). Based on the results of validation tests, one-by-one trials, and practicality tests, the GeoGebra-based coordinate system material learning media for 5th grade elementary school students is suitable for use in the learning process.
Poticanje učenika na čitanje u digitalnom dobu

The aim of this paper is to investigate whether students read, how often they read, who encourages them to read, what they gain by reading, whether they have a city library card and how often they visit it. Furthermore, the aim was to investigate whether digital technology interferes with children, how much time they spend using it and for what purposes, whether they have modern technology at all, and also whether they read e-books and which e-readers they use for that. The thesis also wanted to investigate how teachers today encourage students to read, whether they recommend students books that are not on the reading list and how much their students read such books, whether today's students read more or less than previous generations, whether today's students need more incentives than the previous ones and whether teachers use modern technology, tools and applications in teaching, what kind and how often. In research was used survey questionnaire which was used in printed form (for students) and in digital form (for teachers), and the survey was conducted on a sample of 100 third and fourth grade students of Ivan Mažuranić Elementary School and Josip Kozarac Elementary School in Vinkovci and a sample of 61 teachers from the county area of Vukovarsko-srijemska, Osiječko-baranjska, Brodsko-posavska, Virovitičkopodravska, Šibensko-kninska, Zagrebačka and the City of Zagreb.
Validating Trust in Human-Robot Interaction through Virtual Reality: Comparing Embodied and "Behind-the-Screen" Interactions

Human-agent interaction is commonplace in our daily lives, manifesting in forms ranging from virtual assistants on websites to embodied agents like robots that we might encounter in a physical setting. Previous research has largely been focused on “behind-the-screen” interactions, but these might not fully encapsulate the nuanced responses humans exhibit towards physically embodied agents. To address this gap, we use virtual reality to examine how simulated physical embodiment and the reliability of an agent (automated robotic crane) influence trust and performance in a task simulating a quality assurance role and compare it to a “behind-the-screen” interaction. Out of 119 participants, the data revealed there is a marked behavioral shift observed when reliability hits a 91% threshold, with no influence from embodiment. Furthermore, participants displayed a tendency to trust and defer to the decisions of embodied agents more, especially when these agents were not infallible. This study accentuates the need for transparency about an agent's capabilities and emphasizes the significance of ensuring that the agent's representation is congruent with the nature of the interaction. Our findings pave the way for a deeper understanding of human-agent interactions, suggesting a future where these interactions might seamlessly blend the virtual and physical realms.
Pemanfaatan Arang Tongkol Jagung Sebagai Adsorben Logam Besi (Fe) Dengan Variasi Aktivator Asam

Ferrous metal (Fe) contamination is commonly found in river water around mining sites. One way to reduce the concentration of iron (Fe) in river water is by adsorption. Corncob can be used as a natural adsorbent because it contains carbon compounds (C). This study aims to determine the effect of various acid activators on the adsorption of metal iron (Fe) by corncob waste. Activation of corncob charcoal using hydrochloric acid and sulfuric acid was carried out by soaking for 24 hours. Corncob charcoal was mixed with a 100 mL sample and left for 120 minutes. The results showed that the adsorption capacity of corncob charcoal which had been activated by hydrochloric acid was 0.4143 mg/L and sulfuric acid was 0.4651 mg/L.
Obesity of Information Covid-19 Children Vaccine and Parents Digital Literacy Ability

This study aims to obtain data related to the “tabbayun” attitude of parents in understanding digital literacy in accessing information through social media, especially information on Covid-19 vaccinations for children aged between 06 and 11 years. The purposeless information in our brains can make us lose focus and ultimately make us unable to believe anything. Therefore, it is important to know how tabbayun parents are able to access information, especially the Covid-19 vaccination. Reasoned action theory with quantitative research methods and descriptive approaches chosen for this research, with data collection techniques were carried out through questionnaires, interviews, and supported by documentation, observation, and surveys. This research is expected to be a reference for parents in understanding digital literacy so that they are not trapped in with loads of information, as well as policymakers in conducting socialization and information related to health in the community, especially related to children’s health. To gain an understanding of how parents filter the rapid flow of information so that they are not trapped by inaccurate or even misleading information regarding the Covid-19 vaccination. The results of this study indicate that parents already understand well and are able to filter the information they get. But often, others, including spouses have different understanding patterns, causing debate. And parents also convey vaccination information to children in a language that is easy for children to understand, especially regarding information on Covid-19 vaccination. Impractical information to this literacy ability will be effected to the children vaccination permission and causes parents misunderstanding about the information.&#x0D; Keywords: digital literacy, obesity information, family communication, online media, Covid-19 vaccination
PENGARUH STRATEGI PEMBELAJARAN KOOPERATIF TGT-PUZZLE TERHADAP KECERDASAN SPASIAL-VISUAL PADA MATERI BENTUK MOLEKUL

Abstract: Chemistry is divided into two concepts, namely concrete and defined concepts. Geometry Molecul is one of the defined concepts because the idea is at the molecular level and is invisible to the eye. So, to understand the material molecular shape requires spatial-visual intelligence. Therefore, to determine the effect of the TGT-Puzzle cooperative learning strategy on spatial-visual intelligence on the geometry molecul, this study was conducted. The purpose of this study was to determine whether or not there was an increase in spatial-visual intelligence after being taught using the TGT-Puzzle cooperative learning strategy in class X IPA MA Hidayatul Ummah Pringgoboyo. This study uses an inferential statistical method using the paired sample t-test to determine whether or not there is an increase in visual-spatial intelligence after being taught using the TGT-Puzzle cooperative learning strategy. The analysis technique uses a paired sample t-test with a significance value (sig) &lt; 0.05. The results of this study indicate an increase in spatial-visual intelligence scores after being taught using the TGT-Puzzle cooperative learning strategy, where if there is an increase in the category from average to high average or high average to superior.&#x0D; Keywordsâ€‹: â€‹â€‹ Visual-spatial intelligence, TGT-Puzzle cooperative learning strategy, geometry molecul.
Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online polling system. It provides a novel way to overcome security breaches during the face identity (ID) recognition and verification process for online polling systems. This verification is done by training a pixel-2-pixel <i>Pix2pix</i> generative adversarial network (GAN) for face image reconstruction to remove facial objects present (if any). Furthermore, image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome, thus checking the electorate credentials.
Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality. Nevertheless, a common issue with AP clustering is the presence of excessive exemplars, which limits its ability to perform effective aggregation. This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters, without changing the similarity matrix or customizing preference parameters, as done in existing enhanced approaches. An automatic aggregation enhanced affinity propagation (AAEAP) clustering algorithm is proposed, which combines a dependable partitioning clustering approach with AP to achieve this purpose. The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge. Based on these findings, mutually exclusive exemplar detection was conducted on the current AP exemplars, and a pair of unsuitable exemplars for coexistence is recommended. The recommendation is then mapped as a novel constraint, designated mutual exclusion and aggregation. To address this limitation, a modified AP clustering model is derived and the clustering is restarted, which can result in exemplar number reduction, exemplar selection adjustment, and other data point redistribution. The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected. Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison, and many internal and external clustering evaluation indexes are used to measure the clustering performance. The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.
Local Differential Privacy Preservation via the Novel Encoding Method

In recent years, the application of big data analysis has seen a significant increase across various fields with the aim of extracting useful information from massive data to enhance human life. These data contain a large amount of privacy, and directly using user data for analysis poses a significant risk of privacy leakage. To address this pressing issue, many scholars have incorporated the latest protection methodologies, such as local differential privacy (LDP), into big data analysis. Existing LDP methods typically convert data into binary strings and then add noise, but the weight of each bit unevenly distributes noise, eventually increasing utility loss. Inspired by this, the paper investigates the reduction of error through alternative numeral systems, leading to the proposition of a segmented-based LDP data preservation mechanism (LDPseg), where each bit flip has an equal impact on the outcome. Theoretical analysis reveals that under certain conditions, this mechanism maintains a lower error expectation and variance compared to binary systems. Real-world experimental results indicate that the proposed method exhibits positive performance in machine learning.
Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs

In the realm of PHM, it is common to possess not only process data but also domain knowledge, which, if integrated into data-driven algorithms, can aid in solving specific tasks.This paper explores the integration of knowledge graphs (KGs) into deep learning models to develop a more resilient approach capable of handling domain shifts, such as variations in machine operation conditions.We present and assess a KG-enhanced deep learning approach in a representative PHM use case, demonstrating its effectiveness by incorporating domain-invariant knowledge through the KG.Furthermore, we provide guidance for constructing a comprehensive hierarchical KG representation that preserves semantic information while facilitating numerical representation.The experimental results showcase the improved performance and domain shift robustness of the KG-enhanced approach in fault diagnostics.
Unraveling the NFT economy: A comprehensive collection of Non-Fungible Token transactions and metadata

Non-Fungible Tokens (NFTs) have emerged as the most representative application of blockchain technology in recent years, fostering the development of the Web3. Nonetheless, while the interest in NFTs rapidly boomed, creating unprecedented fervour in traders and creators, the demand for highly representative and up-to-date data to shed light on such an intriguing yet complex domain mostly remained unmet. To pursue this objective, we introduce a large collection of NFT transactions and associated metadata that correspond to trading operations between 2021 and 2023. Our developed dataset is the most extensive and representative in the NFT landscape to date, as it contains more than 70 M transactions performed by more than 6 M users across 36.3 M NFTs and 281 K collections. Moreover, this dataset boasts a wealth of metadata, including textual descriptions and URLs to multimedia content, thus being suitable for a plethora of tasks relevant to database systems, AI, data science, Web and network science fields. This dataset represents a unique resource for researchers and industry practitioners to delve into the inner workings of NFTs through a multitude of perspectives, paving the way for unprecedented opportunities across multiple research fields.
Dual Contrastive Learning Model for Representation Learning and Background Debiasing in SAR Object Classification

As a popular representation learning framework, contrastive learning has been widely used in SAR image interpretation for feature extraction. Recent studies have shown that some features obtained by the model are related to the background information due to the spurious correlation of the backgrounds during the SAR data collection. As a result, the generalization ability of the representation learning model will be greatly affected by the background information and cannot satisfy the requirements of practical applications. This paper constructed a multi-branch dual contrastive learning model that simultaneously compares the similarities between objects and backgrounds of various images. The motivation is to alleviate the impact of the spurious correlation between background information and the object category. The experimental results and interpretable Shapley value demonstrated that the proposed method significantly improved the generalization performance of the SAR object classification algorithm.