Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Networks for Artificial Satellite Image Creation and Manipulation

In the research conducted and reported herein, we demonstrate the ability to generate and manipulate synthetic satellite images by employing a set of deep learning architectures (DL),Synthetic satellite images offer a wide range of potential applications, including the generation of massive, labeled datasets for artificial intelligence (AI) applications, manipulated image detection, and natural disaster monitoring and detection. Although deep learning (DL) architectures have proven to be successful in creating and manipulating natural images and multimedia content, their application in generating synthetic satellite images has not been thoroughly investigated. This can be attributed to variations in semantic content, number of bands, bit, and spatial resolution. To tackle this problem, we will concentrate on two distinct methods of manipulation: global image modifications and local splicing. With an appropriately trained version of cycle GAN architecture, the objective of the land cover transfer is to effectuate a change in the land cover from modified images of vegetation to barren land and vice versa. By employing the pix2pix GAN architecture, the seasonal transfer technique achieves the transformation of a winter-acquired (summer-acquired) satellite image to its summer (winter) counterpart. We use the Alps dataset for seasonal transfer and the land cover dataset for land cover transfer to test the effectiveness of these methods. We describe two types of transformer-based image generation algorithms for local tampering: the image generative pre-trained transformer (iGPT) and the vision transformer. The techniques mentioned above utilize synthetic splices that are incorporated into non-uniform areas of the target image to create modified images, while also ensuring that the splices’ boundaries are not visible. Through the utilization of the SEN12MS and World databases, we have verified the effectiveness of said techniques. We use Sentinel-2 images to demonstrate the validity of the suggested methodologies and emphasize their possible applications and limits.
Using Vocoder Artifacts For Audio Deepfakes Detection

Recent advancements in AI-synthesized human voices have increased the threat of impersonation and disinformation. Detecting synthetic human voices has become crucial to combat these challenges. In this work, we propose a novel approach for detecting synthetic human voices by leveraging the identification of artifacts generated by neural vocoders in audio signals. Neural vocoders are specialized neural networks synthesizing waveforms using temporal-frequency representations such as Mel-spectrograms. These vocoders form a critical component in most deepfake audio synthesis models. Therefore, identifying the presence of neural vocoder processing suggests that an audio sample may have been artificially generated. To harness the potential of vocoder artifacts for synthetic human voice detection, we introduce a binary-class RawNet2 model. This model shares the same front-end feature extractor with the vocoder identification system. We employ a self-supervised representation learning (SSRL) approach, treating vocoder identification as a pretext task. Doing so ensures that the front-end feature extraction module is constrained and optimized to build the final binary classifier for synthetic human voice detection. Through extensive experiments, we demonstrate that the RawNet2 model, trained using SSRL based on vocoder identification, achieves a high overall classification performance on the binary task of detecting synthetic human voices. This approach holds promise in effectively combating the growing threat posed by AI-generated human voice impersonation and disinformation.
Model Poisoning Attack Against Federated Learning with Adaptive Aggregation

Federated Learning (FL) has emerged as a promising decentralized paradigm for training machine learning models across distributed devices, ushering in a new era of collaborative data-driven insights. However, the growing adoption of FL brings forth the need to scrutinize its vulnerabilities and security challenges, particularly concerning adversarial attacks. This book chapter delves into the intricate realm of FL’s susceptibility to adversarial model poisoning attacks and sheds light on the robustness of adaptive federated aggregation methods, including FEDADAGRAD, FEDYOGI, and FEDADAM. Through empirical investigations conducted on diverse image datasets, the chapter provides a meticulous exploration of these state-of-the-art algorithms, unraveling their potential vulnerabilities when subjected to adversarial manipulation. The research unravels the nuanced interplay between adaptive aggregation strategies and adversarial attacks, revealing the strengths and limitations of contemporary security paradigms in federated learning. The findings underscore the critical importance of fortifying FL frameworks with robust defenses to safeguard against adversarial incursions, propelling the field towards more secure, reliable, and resilient distributed machine learning practices. This chapter offers a valuable contribution to the ever-evolving landscape of FL security, enhancing our understanding of the challenges and opportunities that lie ahead.
Refined GAN-Based Attack Against Image Splicing Detection and Localization Algorithms

Recently, researchers have developed new image splicing detection and localization algorithms by leveraging deep learning and contrastive learning techniques. These algorithms utilize Siamese neural networks to detect and localize spliced contents by identifying inconsistencies in an image’s forensic traces. At the same time, deep learning has also enabled the researchers to develop new types of anti-forensic attack that can fool forensic algorithms. Among these attacks, the generative adversarial network (GAN) based approach has demonstrated superior performance in attacking forensic algorithms, including Siamese neural network based forensic algorithms. However, GAN-based attacks can sometimes still fail to fool these forensic algorithms due to the lack of direct control over the distribution of forensic traces in spliced images. In this chapter, we propose a new attack that refine the GAN-based attack on individual spliced images. Our attack achieves this by directly reducing the difference of forensic traces within the spliced images. Through a series of experiments, we demonstrate the capability of our attack to convincingly deceive Siamese neural network based image splicing detection and localization algorithms. The proposed attack significantly improves attack performance and successfully fool the targeted splicing detection and localization algorithms when the GAN-based attack fails.
Exploring Information Privacy Concerns During the COVID-19 Pandemic: A Juxtaposition of Three Models

Government agencies across the globe utilize mobile applications to interact with constituents. In response to the global pandemic, several nations have employed contact tracing services to manage the spread of COVID-19. Extent literature includes various models that explore information privacy. Several researchers have highlighted the need to compare the effectiveness of diverse information privacy models. To fill this gap, we explore the impact of information privacy concerns on citizens’ willingness to download a federal contact tracing app. In particular, we compare three types of prevalent information privacy concerns: global information privacy concerns (GIPC), concern for information privacy (CFIP), and internet users’ information privacy concerns (IUIPC). The results of an online survey administered to 209 citizens reveal that in all models trusting beliefs increase adoption intentions while risk decreases them. However, IUIPC is the only privacy construct that significantly reduces trusting beliefs.
A machine learning-based framework using the particle swarm optimization algorithm for credit card fraud detection

The detection of fraudulent activities in credit cards transactions presents a significant challenge due to the constantly changing and unpredictable tactics used by fraudsters, who take advantage of technological advancements to evade security measures and cause substantial financial harm. In this paper, we suggested a machine learning based methodology to detect fraud in credit cards. The suggested method contains four key phases, including data normalization, data preprocessing, feature selection, classification. For classification artificial neural network, decision tree, logistic regression, naive bayes, random forest while for feature selection particle swarm optimization is employed. With the use of a dataset created from European cardholders, the suggested method was tested. The experimental results show that the suggested method beats the other machine learning techniques and can successfully classify frauds with a high detection rate.
Peran Ruang Baca Rimba Bulan dalam Meningkatkan Kemampuan Literasi Masyarakat Kota Padang Panjang

The presence of a Community Reading Park in the midst of the community is an alternative to increasing the literacy skills of the community and building interest in reading through reading materials and available programs aimed at educating the nation's children. The research aims to see how far the role of the Rimba Bulan Reading Room goes in developing people's reading interests and literacy skills. The type of research used is qualitative research with a descriptive approach. Collecting data using observation, interviews, and documentation The data source is primary data obtained from interviews with TBM managers in the Rimba Bulan Reading Room. Furthermore, the data is processed to test the validity of the data using source triangulation. The results of the study show that the Rimba Bulan Reading Room in Padang Panjang City plays a very important role in developing community literacy with various program activities that have been carried out by involving children and adolescents to read books, participate in programs that TBM carries out such as drawing, reading poetry, playing traditional music, teaching mothers in waste management to become workers, and so on, to improve the economy.
Policymaker Influence on SMKN Kudu Library Development

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This qualitative study examines the impact of policymakers on the enhancement of the Kudu State Vocational High School library's performance. Primary data was gathered through in-depth interviews with key informants including the Principal, Head of Library, and Vice Principals I, II, and III, while secondary data included information and visuals of the library. The findings reveal that policymakers play a crucial role in creating a conducive physical and non-physical environment for the library. The physical infrastructure and technological resources are found to be satisfactory, while non-physical aspects prioritize staff responsibilities. A systematic monitoring system, including performance appraisals by the school principal, is in place, alongside appropriate rewards. Overall, there exists a culture of fairness, safety, and harmonious relationships between library staff, teachers, and leadership. This study highlights the instrumental role of policymakers in optimizing library facilities and management, with potential implications for educational institutions worldwide.&#x0D; Highlight:&#x0D; &#x0D; &#x0D; Policymaker Influence: This study underscores the significant impact of policymakers in shaping the performance and environment of a vocational high school library, emphasizing their crucial role in educational infrastructure development.&#x0D; &#x0D; &#x0D; Balanced Approach: The findings reveal a balanced focus on both physical and non-physical elements, ensuring satisfactory infrastructure and technological resources alongside prioritized staff responsibilities, reflecting a comprehensive approach to library enhancement.&#x0D; &#x0D; &#x0D; Systematic Monitoring and Rewards: The presence of a structured monitoring system, including performance evaluations by the school principal, and appropriate rewards, contributes to a culture of fairness, safety, and positive relationships among library staff, teachers, and leadership, fostering a conducive educational environment.&#x0D; &#x0D; &#x0D; Keyword: Policymakers, Vocational High School, Library Performance, Qualitative Study, Educational Institutions&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Enhancing Campus Familiarity: A Comparative Study of Augmented and Mixed Reality Applications for UMSIDA Campus 2

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study addresses the need for improved orientation among Muhammadiyah University of Sidoarjo (UMSIDA) students, particularly newcomers unfamiliar with the layout of Campus 2. Leveraging the Multimedia Development Life Cycle (MDLC) Method, researchers developed and compared Augmented Reality (AR) and Mixed Reality (MR) applications, utilizing Faculty of Science and Technology brochures to render 3D representations of UMSIDA Campus 2 buildings. An online questionnaire garnered feedback from 25 UMSIDA students, with 88% expressing approval for the AR-based introduction application. To better assess its impact on prospective student enrollment, PMB Team members were consulted, resulting in 66.7% favoring the MR application for its enhanced engagement. Notably, both AR and MR applications were deemed equally useful by 66.7% of respondents. Consequently, these applications will be integrated into a comprehensive solution for further evaluation and optimization. This research underscores the potential of immersive technologies in enhancing campus navigation and highlights the significance of user preference in educational applications of augmented and mixed reality.&#x0D; Highlight:&#x0D; &#x0D; Utilizing MDLC Method: The study leverages the Multimedia Development Life Cycle (MDLC) Method for the development and comparison of Augmented Reality (AR) and Mixed Reality (MR) applications, targeting improved campus orientation for UMSIDA students.&#x0D; User Preference in Immersive Technologies: Feedback from UMSIDA students and PMB Team members revealed a preference for the Mixed Reality (MR) application, emphasizing the significance of user preference in educational applications of augmented and mixed reality.&#x0D; Integration for Enhanced Effectiveness: The AR and MR applications will be integrated into a comprehensive solution, highlighting the commitment to ongoing evaluation and optimization to further enhance campus navigation for UMSIDA students.&#x0D; &#x0D; Keyword: Orientation, Augmented Reality, Mixed Reality, Campus Navigation, Immersive Technologies&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Improving the Reading Literacy Culture of Islamic Elementary School Students in Pemalang, Indonesia

Reading literacy is a fundamental skill for everyone. This can be instilled from a young age. The Government of the Republic of Indonesia, through the Ministry of Education and Culture, issued Regulation Number 23 of 2015 on the Development of Student Characteristics. This policy forms the foundation for the School Literacy Movement program, which is intended to increase students' interest in reading and cultivate literacy. The purpose of this study is to analyze efforts to improve students' reading literacy culture at Madrasah Ibtidaiyah Dewi Masyithoh 02 Banyumudal Pemalang Indonesia through the School Literacy Movement program. The descriptive qualitative approach was used in this study. The study lasted from 18 October 2022 to 12 January 2023. Data was gathered through observation, interviews, and documentation. The data analysis technique includes three steps: data reduction, data display, and data verification. The findings revealed that several efforts were made in the classroom to improve students' reading literacy culture, including implementing a habituation program to read books before beginning learning, reading asma'ul husna, reading salawat, memorizing juz amma, providing reading corners, posterization, and procuring existing literacy trees. The literacy program was introduced in two stages. The first stage was carried out by reading asma'ul husna and salawat in the school yard, and the second stage was carried out by reading juz amma and developing reading habits before learning began. The impact of implementing the program is that students become accustomed to reading. The reading literacy program should be integrated into other school-based and community-based programs.
Comparative Analysis of Animal Structure Practicum Results Based on Online and Offline Knocking Systems

Animal structure lectures are a learning process that is integrated with practicum activities which are mandatory activities and must be carried out in one semester in tandem with the implementation of theoretical learning. This study aims to find out the comparison of student midterm exam scores using the online and offline ketok system. The method used is a comparative analysis of midterm exam scores in two different classes. The instrument used was data on student midterm exam scores in two animal structure practicum classes. The results showed that the average midterm exam score in the class with the online knock system was 52.9 with the highest score 80 and the lowest 30, while in the offline knock system class it was 47.3 with the highest score 80 and the lowest 25 out of a total sample of 22 students in each class. Based on the research results, it can be concluded that the different midterm exam methods in the animal structure practicum have a significant influence on the practicum exam results
The Effect Of Using Multimedia Macromedia Flash On Student Mathematics Communication At SMP Negeri 1 Labuhan Deli

This research is a quasi-experimental research. The research conducted at SMP Negeri 1 Labuhan Deli aims to find out. 1) To find out how the mathematical communication ability of students who learn to use Macromedia Flash. 2) To find out how the mathematical communication ability of students who learn without using Macromedia Flash. 3) To find out whether the mathematical communication of students who learn using Macromedia Flash media is better than the mathematical communication of students who learn without using Macromedia Flash media for class VII students at SMP Negeri 1 Labuhan Deli T.A. 2021/2022. The population in this study were all students of class VII at SMP Negeri 1 Labuhan Deli. Using a homogeneous technique, samples were taken from 2 classes of population. One class was used as the experimental class, namely class VII-2 which was taught with Macromedia Flash and class VII-3 which was used as the control class, namely class which was taught without using Macromedia Flash. As a data collection tool using PreTest and PostTest. In this study 1) the mathematical communication ability of students learning to use Macromedia Flash with an average pretest of 67.77 and a posttest of 85.54. 2) the mathematical communication ability of students who learn without using Macromedia Flash with an average pretest of 67.13 and a posttest of 77.54. then testing the hypothesis for learning outcomes using paired t-test. after testing the data, it turns out that the results of testing the ability of learning outcomes in the experimental class at the pretest level α = 0.05 with Lcount &lt; Ltable, namely 0.09452 &lt;0.192 and posttest 0.1228 &lt;0.192 , and the results of testing the ability of learning outcomes in the control class at the pretest level α = 0.05 with Lcount &lt; Ltable, namely 0.1014 &lt; 0.184 and posttest 0.1659 &lt; 0.184, then H0 is accepted and Hi is rejected. Thus it can be concluded that the mathematical communication ability of students who use macromedia flash media are better than those without using macromedia flash media.
Public Administration Study Program Needs for Public Speaking Ability in English to Improve Graduate Quality

This study aims to find the needs of the Public Administration Study Program related to Public Speaking to prepare graduates who are competent in speaking in public using English as a provision to face the global era. There is a desire and hope that graduates of the Public Administration Study Program have global competitiveness in addition to science related to administration. To maximize the potential of the next candidate for Public Administration graduates, a material design is needed to improve Public Speaking skills by first analyzing the Public Speaking skill needs that are most needed by the study program. This study analyzes the needs of expected materials from a semi-structured interview with the Head of the Public Administration Study Program. The results of the research show that the Public Administration study program wants to focus on learning objectives on improving communication skills based on the future job needs of graduates seen through tracer studies.&#x0D; Highlights:&#x0D; &#x0D; Effective Communication in English: Enhancing public speaking skills in English is crucial to prepare graduates for the global era and increase their international employability.&#x0D; Job-Oriented Learning Objectives: The study program aims to align public speaking materials with the specific needs of future jobs, identified through tracer studies, to make graduates more job-ready.&#x0D; Competency Beyond Administration: While the program focuses on administration knowledge, it also emphasizes developing well-rounded communication abilities, enabling graduates to excel in diverse professional settings.&#x0D; &#x0D; Keywords: Public Administration Study Program, Public Speaking, Global Competitiveness, Material Design, Communication Skills
Overcoming Barriers in Improving Literacy and Numeracy at Bulusari 1 State Elementary School, Pasuruan Regency.

This scientific article presents a community service activity aimed at addressing barriers to improving students' literacy and numeracy at SD Negeri Bulusari 1 Kec. Gempol, Kab. Pasuruan. The primary challenge faced by teachers was their limited knowledge in literacy and numeracy. To tackle this issue, a solution was implemented through literacy and numeracy material socialization. The study reveals that this socialization initiative significantly enhanced teachers' understanding of literacy and numeracy concepts and their ability to design engaging literacy and numeracy activities. The results highlight the potential of material socialization as a practical and effective strategy to empower educators and positively impact students' learning outcomes.&#x0D; Highlights:&#x0D; &#x0D; The study focuses on a community service activity that addresses the challenges of enhancing literacy and numeracy among students, particularly at SD Negeri Bulusari 1 Kec. Gempol, Kab. Pasuruan.&#x0D; A key aspect of the solution is the implementation of literacy and numeracy material socialization, which significantly improves educators' understanding of these subjects and their ability to design effective activities.&#x0D; The research highlights the potential impact of material socialization as a practical strategy to empower educators and positively influence students' learning outcomes.&#x0D; &#x0D; Keywords: Community service, Literacy and numeracy, Material socialization, Educators, Students
Pengenalan Pola Garis Telapak Tangan Menggunakan Metode Fuzzy K-Nearest Neighbor

Palm pattern recognition is an important topic in the field of pattern recognition and biometrics. This study aims to develop a palm line pattern recognition method using the Fuzzy K-Nearest Neighbor (Fuzzy KNN) method to improve the accuracy and effectiveness of the individual recognition system. The research was conducted by collecting images, performing segmentation, feature extraction, and implementing the Fuzzy KNN method. Based on the results of the experiments that have been carried out, it can be seen that the average accuracy obtained is 20%, which means that the system is less able to recognize palm line patterns. This is due to a lack of dataset which causes poor recognition performance on the model.