Reviewerly/exHarmony huggingface dataset:
Social Media Literacy Assistance to Ponorogo Instagram Admins as Citizen Journalism Activities

Instagram administrators @ponorogo.update, @setenpo, @iloveponorogo, and @infoponorogo have played an active role in an organic movement of citizen journalists in Ponorogo. This Instagram admin has actively amplified Ponorogo locality-themed information that residents need, especially during the COVID-19 pandemic. Instagram in Ponorogo has been accessed with a high engagement rate. Besides that, the Instagram admin acts as a curator of information and has been able to determine the behavior patterns of netizens in Ponorogo. The social media literacy assistance service uses the Community-Based Participatory Research (CBPR) method, which consists of laying the foundation, planning research, gathering and data analysis, and acting on findings. The CBPR method is then synthesized with a theoretical framework for digital literacy levels. The result of this service program is that the Instagram Ponorogo admin and the service team succeeded in formulating a guidebook for managing Instagram as a medium for disseminating information on Ponorogo residents. This social media literacy guide booklet has been synchronized based on social media regulations and can be used applicatively. All stages of community service are practical strategic steps so that Ponorogo residents are accustomed to receiving credible and valid information (a well-informed society) and minimizing the occurrence of misinformation, disinformation, and hoaxes.
ANALISIS EFEKTIVITAS DAN EFISIENSI METODE ENCODING DAN DECODING ALGORITMA BASE64

This study discusses the methods of encoding and decoding the base64 cryptographic algorithm. The term encoding means a process of converting plaintext into ciphertext. While decoding is a term that means the process of turning ciphertext back into plaintext. In general, classical cryptography has been abandoned because of its logic which can be easily solved, so now it has switched to modern cryptography which operates in bit-binary mode which is formed from ASCII code. The base64 algorithm is a form of modern cryptography which is included in the modern type of block cipher cryptography. This study aims to obtain a comparison between 3 types of tools in carrying out a cryptographic encode and decode process on the base64 algorithm. The 3 types of tools include the Base64 Python Program, Bash Base64 Kali Linux, and CyberChef which are online tools. This type of research method uses qualitative methods because it tends to use analysis in research. Where the results of the first comparison are used to make it easier to see the accuracy of the encoding and decoding processes and the results of the second comparison are used to compare in terms of time efficiency when encoding the three tools using 1,000 words which will continue to be added by 4,000 words, up to a total of 9,000 words . These words are taken using dummy text (lorem ipsum).
Story Book – A Social Media-based Web Application

In this article, the MERN (MongoDB, Express, React, and Node.js) stack is used to create and deploy a social networking platform. The project’s goal was to provide a feature rich platform with tools for users to share text and images, connect with others via friend requests, engage with postings through likes and comments, and flag offensive material. Additionally, the platform provides options for dark and light viewing modes. In addition to highlighting the important features and functionalities developed, the paper gives an overview of the project’s architecture and goes over user registration, profile setup, post authoring, friend request management, and post engagement. The specifics of the implementation, including the difficulties encountered and fixes used, are covered. The platform’s review reveals its effective operation, user involvement, and responsiveness. The study advances the subject of social media platforms by demonstrating how a MERN based solution may be implemented successfully. The results of this study act as a starting point for future developments in social media platform design.
Reconstruction of Neural Radiance Fields With Vivid Scenes in the Metaverse

With the rapid development of the metaverse, AR/VR technology and consumer electronics have emerged as crucial drivers for future virtual experiences. In this context, realistic 3D scene modeling and rendering has become particularly critical. Neural Radiance Fields (NeRF), as a deep learning-based method, have made significant progress in the reconstruction and rendering of real-world 3D scenes. However, NeRF still faces challenges in efficiently handling high-frequency information within the object, leading to issues such as blurry details and artifacts. To address this issue and provide more immersive virtual experiences, we propose Vivid-NeRF, which offers benefits to both metaverse and AR/VR technologies. Vivid-NeRF extracts information at different frequencies from the image and fully exploits the high-frequency information in the 3D feature generation process to obtain more realistic details. In addition, we propose frequency-based sampling to increase the sampling of high-frequency components. Finally, we merge frequency information with viewpoint features obtained through frequency-based sampling to enhance the model’s capability in expressing scene details. With these improvements, Vivid-NeRF significantly reduces surface blur and accurately captures and reproduces the smooth surface appearance. We conduct experiments on the Blender and Shiny-Blender datasets. Experimental results demonstrate that Vivid-NeRF achieves PSNR of 35.00 and 34.01, SSIM of 0.976 and 0.970, and LPIPS of 0.033 and 0.064. Both quantitative and qualitative assessments demonstrate that our approach outperforms the previous state-of-the-art (SOTA) ABLE-NeRF.
A KNOWLEDGE MANAGEMENT GUIDELINE: IDENTIFYING AND QUANTIFYING THE KNOWLEDGE LEVEL OF A TEAM THAT WORKS FOR A SOFTWARE ENGINEERING PROJECT

This scientific paper aims to explore the identification and quantification of the knowledge level within a team working on a software engineering project. The knowledge level of a team plays a crucial role in the success and efficiency of project outcomes. To achieve this objective, a comprehensive review of existing literature on knowledge management, team dynamics, and software engineering practices was conducted. This article reveals insights into the knowledge dynamics within software engineering teams. The research methodology consists of different quantitative and qualitative methods for data collection and analysis. The identified knowledge gaps and strengths can inform strategies for improving team performance, such as targeted training programs or knowledge sharing initiatives. Furthermore, the quantification of knowledge levels can serve as a benchmark for future projects, allowing for better resource allocation and team composition. Overall, this research is bringing benefits to the field of software engineering by providing a systematic approach to identify and quantify the knowledge level of teams, enabling organizations to optimize their team structures and enhance project outcomes.
MobileVitV2-Based Fusion of Vision Transformers and Convolutional Neural Networks for Underwater Image Enhancement

Acquisition of underwater images presents chal-lenges stemming from inherent distortions and the degradation of color and contrast within the aquatic environment. These challenges, characterized by image quality issues, pose significant obstacles to the training of supervised deep learning models on extensive and diverse datasets, potentially constraining the overall performance of the model. High-quality underwater images play an important role in the development of Autonomous Underwater Vehicles(AUVs}. In this work, we present MobileVitV2 Block Fusion Augmentor and Global-Local Feature Fusion Block with Attent-UNet, which are offered as blocks of generator. Through extensive experiments on different datasets, our proposed method achieves better visual quality, and the method can obtain better Quality images in poorer underwater environments.
Synthetic Data Generation for Engineering Education: A Bayesian Approach

Engineering education research often requires large amounts of data that can be time-consuming and costly to collect. In response to these challenges, synthetic data generation has emerged as a pragmatic solution across various industries, allowing researchers to sidestep the complexities associated with data collection and preprocessing, thereby streamlining the focus on actual model implementation. Despite the widespread adoption of synthetic data in diverse domains, its integration into engineering education research remains relatively underexplored.This paper aims to fill this gap by proposing a novel method for synthetic education data generation through the application of Bayesian approaches. The proposed approach involves leveraging a domain knowledge base to construct a Bayesian network and its corresponding Conditional Probability Tables (CPTs). The subsequent use of Gibbs sampling facilitates the generation of synthetic data, enabling a direct comparison between the characteristics of the original and synthetically generated datasets.
Knowledge of cybersecurity against social cybercrime of female high school students

The dangerous consequences of the use of cyberspace are increasingly greater and constant, regardless of the age range or educational level. Young people use the Internet daily to carry out various activities, and they do not always know the risks to which they are exposed due to cybercrime. The study’s objective is to analyze the relationship between the level of knowledge about cybersecurity and the prevention of social cybercrime in secondary education female students in the year 2023. Basic cross-sectional descriptive correlational research was carried out that included a questionnaire with a Likert scale that was applied to 48 third-year female students—years of secondary education. To measure the results, the Rho Spearman was used, finding a significant correlation (p=0.0010<0.001) between the ordinal variables X: level of knowledge about cybersecurity, and Y: degree of prevention of social cybercrime. It was concluded that knowledge of cybersecurity prevents cyberstalking and cyberbullying.
Federated Learning Integration in O- RAN: A Concise Review

The rapid growth of the telecommunication industry presents a global challenge in maintaining data security and privacy amid increasing data traffic and diverse applications. Applying Federated Learning (FL) to the upcoming Next Generation Wireless Networks (NextG) or Open Radio Access Network (O-RAN) holds great potential as a solution for addressing these challenges. With this in consideration, our paper explores a secure and privacy-conscious solution, focusing on the potential of FL in upcoming wireless networks or O-RAN. FL's cooperative learning approach ensures data confidentiality, offering significant advancements in security issues associated with growing user numbers, and supports the migration to the NextG. In this paper, the concise review provides valuable insights into O-RAN, FL, and related works, with an emphasis on security and privacy. Additionally, it explores framework utilization and outlines future research directions for integrating FL within O-RAN. This approach aims to offer the readers a quick and clear understanding of FL integration within O-RAN, avoiding the need to navigate through extensive survey papers.
Employee Presence and Payroll Information System Using Quick Response Code and Geolocation

The presence in the institution still uses the conventional method of affixing a signature on the attendance sheet. Presences that have been carried out so far are felt to be less effective and efficient because sometimes attendance is filled towards the end of the month, which causes the validity of attendance data to be questioned. Even errors are often found in the recording, which causes the nominal to be inappropriate and must be revised. This research aimed to design an information system using the Quick Response Code to increase the effectiveness of employee attendance and payroll, supported by geolocation, to make it more efficient. The method used in this research was the waterfall method, using the stages of communication, planning, modeling, construction, and deployment. This research produced an information system that could make it easier for employees to attend, speed up determining employee salaries and filing financial disbursements, and increase employee presence and salary validity. The test results showed that 90% said they were satisfied with the performance of the system being built.
Hardware Efficient Edge-Detection Based Color Filter Array Interpolation for Industrial Cameras

In manufacturing and industrial inspection, digital cameras are widely used for production monitoring, warehouse management, and detecting packing defects. Digital cameras require three separate image sensors to capture full-color images. However, due to the high cost of image sensors, many digital camera manufacturers are using a single image sensor with a color filter array (CFA), thereby reducing the overall cost of the camera system. In this paper, a computationally efficient algorithm for color filter array (CFA) interpolation is specifically designed to optimize hardware resource utilization. The algorithm focuses on utilizing simple additions, subtractions, comparisons and shift operations. Furthermore, an efficient hardware architecture for the proposed algorithm is designed and modeled using Verilog HDL. It is then synthesized using the Cadence Genus compiler with 90nm Generic Process Design Kit (GPDK 90nm) CMOS technology. The results of simulations and synthesis reveal that the proposed method is computationally efficient and requires less hardware resources as compared with the existing work making it suitable for the applications mentioned above.
Pelatihan Menulis Karya Ilmiah Pada Era Digitalisasi : Analisis Kemampuan Siswa Di Man 1 Tanjung Pura

&#x0D; &#x0D; &#x0D; &#x0D; The activity of scientific writing training at MAN 1 Tanjung Pura is a form of community service, considering the importance of soft skills in writing various scientific works today. The method used is face-to-face training with stages of opening, motivation, materials, and evaluation. The training in scientific writing in the era of digitalization encourages students to be actively engaged in writing activities. The objective of this training is to provide the necessary skills and knowledge for students to write scientific works effectively. During this training, students are taught various types of scientific works and how to access and evaluate information sources through digital technology. The use of software and applications is also introduced to assist students in composing, organizing, and publishing scientific works digitally.&#x0D; &#x0D; &#x0D; &#x0D;
A Certificate Management Mechanism Using Distributed Ledger

Public Key Infrastructure (PKI) is a system that provides a secure method of exchanging digital information by authenticating the identity of the parties involved and ensuring the confidentiality, integrity, and non-repudiation of the data being exchanged. However, in recent years, attacks on CAs in PKI have been rampant, such as CA intrusion and the issuance of fraudulent certificates, causing concerns during communication. As an emerging solution, distributed ledger technology provides data traceability and immutability, solving the issue of the original PKI lacking transparency and the difficulty in immediately detecting the issuance of fraudulent certificates. In this paper, we propose a distributed ledger technology-based certificate management mechanism for issuing, revoking, and updating certificates, and also providing a secure method of communication by using certificate authentication to verify identity and ensuring communication security.
Development of the Edge Computing Platform based on Modular Architecture using Intel SGX

With the proliferation of IoT devices, the number of devices connected to the Internet has been rapidly increasing. An edge computing platform must flexible and efficient data control. Also, edge nodes are not always reliable. Edge node administrators can leak data through intentional mishandling. In this paper, we propose an edge computing platform on modular architecture that protects data and processing from interception and a processing flow based on data characteristics using Intel SGX and multi-authority attribute-based encryption. In addition, we report a performance evaluation of our method.
A Method for the Evaluation of Uncertainty in the Measurement of Transformer Capacity

The evaluation and expression of uncertainty is one of the fundamental issues in metrology. Transformers are not only widely used in power systems, but also one of the key and expensive components. This paper studies the measurement principles and evaluation methods of uncertainty in capacity measurement of transformers. It proposes a supplement to the uncertainty evaluation method for capacity measurement of power transformers in the Guide to the Expression of Uncertainty in Measurement (GUM) and its application. Finally, the paper analyzes the effects of different factors on measurement uncertainty and obtains the expanded uncertainty of transformer capacity measurement. The research results are of great significance for improving the quality of transformer capacity measurement.
Arbitrary-Scale Image Super Resolution Using Residual In Residual Dense Networks With New Local Implicit Image Function

With the development of deep convolutional neural networks, the technic of Image super resolution makes a remarkable progress. While most researchers focus on training one model with one kind of scale, only some focus on making a model that can be adapted to any scale. We refer to prior work on a single model arbitrary resolution and improve on it. The model we refer to is an auto-encoder structure. The encoder is used to get the feature maps of the image we input, and the decoder is used to reconstruct these feature maps to any resolution the user is specific to. With the experiment results, we show that our model RRDN-NLIIF, which is short of Residual in Residual Dense Networks with New Local Implicit Image Function, makes a better performance than the one we refer to on PSNR metric.
A Keyword Transition Extraction Method for Time-series Text Data and Its Application to Discovering the Transition of Key Technology Elements in Japan

This paper presents a keyword transition extraction method for time-series text data and its application to discovering the transition of key technology elements in Japan. Generally, capturing trends in science and technology is one of the most important issues. To capture these trends, when a method for extracting important keywords year by year from white papers published annually by the government can be established, it becomes possible to visualize the trends in science and technology. This method extracts words from text data for each time, and derives F-Score as a measure of the likelihood of occurrence of each word at that time. By extracting the transition of keywords for each time from the change in F-Score for each word, it is possible to extract the transition of keywords for each time. By realizing this method, it becomes possible to discover important keywords for each era from time-series text data and visualize the transitions of those keywords.
THE PLACE AND ROLE OF KNOWLEDGE MANAGEMENT IN A SUSTAINABLE ORGANIZATION FROM THE SOFTWARE ENGINEERING BRANCH

The tremendous growth of both market and customer requirements determines the projects in the software engineering branch to be characterized by complexity, dynamic and agility. As the significant results of the projects lead further to the sustainability of the organization, an important indicator of evolution is represented by how knowledge is treated within the organization and further transferred to the outside. There are multiple ways to operate projects from the software engineering branch and most of them are based on documented academical approaches as Agile Methodology or Waterfall Model, which present a different set of characteristics. However, this article studies, in an exploratory framework, how knowledge is gathered based on the approach of the project. This investigation might come at hand for those professionals that are willing to improve the project management framework by addressing those areas where a deep analysis of each process could help building a more efficient knowledge management in the organization. In addition to the contributions of understanding what’s happening with the knowledge in software engineering projects, this investigation can be used, as well, for researches about an organization’s sustainability.
Implementation of a Convolutional Neural Network (CNN)-based Object Detection Approach for Smart Surveillance Applications

In the realm of smart surveillance systems, a fundamental technique for tracking and evaluating consumer behavior is object detection through video surveillance. While existing research underscores object detection through deep learning techniques, a notable gap exists in adapting these methods to effectively capture and recognize small, intricate objects. This study addresses this gap by introducing a customized methodology tailored to meet the nuanced requirements of accurate and lightweight detection for small objects, especially in scenarios prone to visual complexity and object similarity challenges. The primary objective is to furnish a vision-based object identification method designed for surveillance applications in smart stores, with a particular focus on locating jewelry objects. To achieve this, a Convolutional Neural Network (CNN)-based object detector utilizing YOLOv7 is employed for precise object detection and location extraction. The YOLOv7 network undergoes rigorous training and verification on a unique dataset specifically curated for this purpose. Experimental results affirm the efficacy of the proposed object identification method, demonstrating its capacity to detect items relevant to smart surveillance applications.
PoMC: An Efficient Blockchain Consensus Mechanism for the Agricultural Internet of Things

Blockchain-based agricultural IoT systems face key challenges such as high delay and low transaction throughput. Existing complicated consensus mechanisms can cause IoT devices work inefficiently due to the limited computing, storage and energy resources. Additionally, many message exchanges can lead to high latency in the consensus process, which hinders the real-time applications of the agricultural IoT. Therefore, we propose Proof-of-Multifactor-Capacity (PoMC), an efficient and secure consensus mechanism for the agricultural IoT. It uses the communication capacity and credibility of a node as the evidence for making consensus. Moreover, a senator node lottery algorithm based on a credit mechanism and a new distributed incentive mechanism are designed to enhance security and motivate nodes to actively maintain the system. This paper analyses the performance of PoMC theoretically, including security, latency and system throughput, and presents a comparison of its asymptotic complexity with some existing consensus mechanisms. The simulation results demonstate that the average transaction validation latency and average consensus latency of PoMC have decreased by 10% and 23%. In addition, PoMC outperforms SENATE, PoQF and PBFT by 56%, 60% and 64% in terms of the system throughput, respectively.