Reviewerly/exHarmony huggingface dataset:
Media Informasi untuk Perawatan Kucing berbasis Android

The use of information technology so far is not only in the field of education, but can also be utilized in the health sector, such as cat care. Raising a cat has different needs and ways of caring for other animals such as feeding, drinking, shelter, and health. This study aims to produce android-based cat care information media, knowing the feasibility and response of users. This type of research is research and development using the Rapid Application Development (RAD) model which consists of 3 stages, namely: requirements planning, RAD design workshop, and implementation. The subjects in this study amounted to 19 cat owners to find out the user's response. Data collection techniques using questionnaires and data analysis using quantitative descriptive. Our findings show that the application developed is in the form of Android-based cat care information media. The material expert due diligence result is 96.3% (very feasible). Meanwhile, the results of the due diligence of media experts obtained 87% (very feasible). Thus this application is worthy of being used as an android-based cat care information media. So that this application can help some parties or cat owners to take care of their cats without having to go to a veterinary clinic.
Marketplace Logistics via Answer Set Programming

Marketplaces aggregate products from several providers and handle orders involving several suppliers automatically. We report on an application of Answer Set Programming for automatically selecting products from different warehouses within the network of a marketplace to fulfill a given order. The presented solution easily accommodates different objective functions that are employed at different stages of managing an order, enabling economic savings for the customer as well as simplification of logistics for both the marketplace and its suppliers.
Enhancing Cybercrime Detection Through Big Data Analytics: A Conceptual Framework

The growing complexity of cybercrime reduction and prevention difficulties necessitates a different approach to dealing with the massive amounts of data involved. The issues of reducing and preventing cybercrime are becoming more complex as cybercrime beco mes ingrained in our daily lives. When it comes to showcasing the original division of criminal activities, traditional police operations typically fall short, which means they contribute less to the right deployment of police resources. This study explores strategies for anticipating cybercrime. These techniques include assessing the geological zones that provide a higher level of danger but are outside the scope of standard enforcement powers using Hadoop technology for big data analytics. The method used an algorithm for topographical cybercrime mapping to pinpoint regions of the country with disproportionately high rates of cybercrime. By isolating extreme occurrences of cybercrime clusters, this strategy will shed light on recurring cybercrime trends. Clusters of cybercrime incidents can be used to illustrate common cybercriminal behaviour. The processing capability of the Hadoop platform is a key factor in the enhanced estimation technique. Cybercrime is a global threat because of the increasingly sophisticated tactics criminals employ to breach security systems and steal private information. Recent years have seen promising developments in the use of machine learning, deep learning, and transfer learning for the detection and prevention of cybercrime. Then, we look at transfer learning, early-stage algorithmic cybercrime prediction learning, and active and reinforcement learning. Finally, we synthesise our results and talk about potential future research areas for cybercrime.
Improving Hand Pose Recognition Using Localization and Zoom Normalizations over MediaPipe Landmarks

Hand pose recognition presents significant challenges that need to be addressed, such as varying lighting conditions or complex backgrounds, which can hinder accurate and robust hand pose estimation. This can be mitigated by employing MediaPipe to facilitate the efficient extraction of representative landmarks from static images combined with the use of Convolutional Neural Networks. Extracting these landmarks from the hands mitigates the impact of lighting variability or the presence of complex backgrounds. However, the variability of the location and size of the hand is still not addressed by this process. Therefore, the use of processing modules to normalize these points regarding the location of the wrist and the zoom of the hands can significantly mitigate the effects of these variabilities. In all the experiments performed in this work based on American Sign Language alphabet datasets of 870, 27,000, and 87,000 images, the application of the proposed normalizations has resulted in significant improvements in the model performance in a resource-limited scenario. Particularly, under conditions of high variability, applying both normalizations resulted in a performance increment of 45.08%, increasing the accuracy from 43.94 ± 0.64% to 89.02 ± 0.40%.
Challenges of generative artificial intelligence for the higher education system

Problem statement . The theoretical and technological challenges of using generative artificial intelligence (AI) in the higher education system of the Russian Federation are briefly discussed. Methodology. System-structural and system-activity approaches are used. Content analysis and thematic monitoring of generative АI technologies were carried out, its constructive, cognitive and pedagogical features were revealed. Results. The features of generative AI are analyzed. The digital transformation of education is shown through a rethinking of the key roles of teachers in the digital era in the direction of educational engineering and the development of creative competencies of students. A generalized description of the challenges of generative AI in relation to universities is given. Several possible ways of identifying and neutralizing the use of generative AI by students in the implementation of practical tasks are suggested. The ways of solving the problems of using generative AI for universities are substantiated: a) cloud computing and the use of ready-made models; b) cooperation with industry experts; c) the use of interdisciplinary approaches; d) encouraging experimentation, creativity and team building; e) providing ongoing support and mentoring; f) solving ethical problems of using generative AI in higher education. Conclusion. It is proved that the paradigm of “educational engineering”, including the use of generative AI, focuses on the development of creative design and design competencies of students and teachers.
Sistem Keamanan Work From Anywhere Menggunakan VPN Generasi Lanjut

&#x0D; &#x0D; &#x0D; &#x0D; Working remotely or what we know as Work From Anywhere is still a trend after the COVID-19 pandemic. Working from anywhere and anytime can increase productivity and reduce transportation costs, and is accompanied by fast internet support, good communication and collaboration platforms so this is very popular and in demand. When implementing WFA, it has its own challenges, namely data security. Not all organizations are supported by a reliable IT infrastructure that can protect employee data during WFA. One of the security system technologies that can be used during WFA is VPN. Currently there are many VPN protocols that can be used for WFA. However, in implementing a VPN network, there are several things that must be considered to ensure that the VPN network is successful and functional, such as security, choosing the right architecture, selecting technology and protocols, scalability, quality of service, management and monitoring, as well as security and privacy policies. ZeroTier is a next-generation VPN that is easy to configure, can support multiple devices, and uses an end-to-end connection, eliminating the need for a centralized VPN server. In implementing VPN ZeroTier the method used is the Network Development Life Cycle (NDLC). This methodology is used to plan, implement, and manage a VPN network with ZeroTier to function according to WFA needs.&#x0D; &#x0D; &#x0D; &#x0D;
Paleolithic Hide Processing Activities

Hide, one of the various work materials used by the people from the Paleolithic onwards, is an essential material for making cloths and tents for survival. The technology and methods of hide processing and use, however, might be understood based on archaeological data and ethnoarchaeological studies because there aren’t many direct hide-related evidences in Paleolithic sites.&#x0D; Researchers from Korea and abroad have undertaken studies on the use-wear analysis of the end-scraper, and they have found that different work materials, including wood, bone, antler, and hide, leave different traces behind. Of them, the trace formed by hide occupies the majority. Therefore, it can be known that the end-scraper was a specialized tool for processing hide. Ethnoarchaeological studies on this subject show the various stages of hide processing and usage: hunting animals; several stages of processing hide; and use. End-scrapers have been recovered from a lot of sites in the Paleolithic. This fact indicates that hide processing and use were essential for surviving in that era. Depending on the period, region, environment, animal species, purpose of hide use, type of end-scraper, its use method, and hide processing method, they are slightly different. These led to the establishment of the local technological tradition. These facts shed light on the different aspects of the hide processing method at the local level.
Student’s Cognitive Engagement in Teaching Vocabulary through Mnemonic Strategy

This research aims to explore students' cognitive engagement and find out student’s perception towards learning vocabulary through mnemonic strategy. This research is a qualitative research with TPT cognitive engagement model as research design. The data of this research was gathered for four meetings by using observation, interview, and documentation. The data of observation were analyzed by interactive analysis supported by quadrant and tally analysis. The researcher take five participants from 10th grades from one of Islamic senior high school MAN 2 Kuningan. The findings showed that students have positive cognitive engagement where all of the participants get in the 4th quadrant except for one. Students Cognition level improved throughout the time until they have high cognition and high participation. Students also showed positive perceptions where they feel happy and fun during learning because they considered mnemonic strategy is like a game. Students also stated that their thinking skill is improved by the learning. Mnemonic strategy is also suggested to be implemented as a method in learning vocabulary in schools because it is effective.
ANALISIS KEPUTUSAN MENGGUNAKAN METODE CUT OFF POINT DAN FUZZY SIMPLE ADDITIVE WEIGHTING DALAM MENENTUKAN JENIS LAPTOPTERBAIK PADA MAHASISWA MATEMATIKA FMIPA UNIVERSITAS NEGERI PADANG

The emergence of various types and brands in the laptop industry poses problems for consumers in determining the best type of laptop. To assist consumers in determining the best type of laptop, a Decision Support System is needed. This research is an applied research. The data used is primary data obtained from the distribution of questionnaires through Google Form to students of the Department of Mathematics UNP batch of 2020, 2021, 2022. The variables used are Brand, Operating System, Processor, Solid State Drive, Random Access Memory, Battery, Screen Size, Laptop Weight, Color, Price. The methods used in this study are the Cut Off Point Method and the Simple Additive Weighting method. Based on the results of the problem analysis in determining the best laptop type using the Cut Off Point and Fuzzy Simple Additive Weighting methods, it shows that the Asus zenBook Pro laptop type is recommended as the best type of laptop with a preference value of 0.87
FedGda: Model Aggregation Algorithm Based on Topological Density in Federated Graph Learning

Graph neural networks (GNN) show excellent results in the downstream missions involving graph data in non-Euclidean spaces. Meanwhile, as user concerns about data privacy and security continue to grow, GNN leads the rapid development of Federated Graph Learning (FGL). FGL employs a distributed structure to train a graph machine learning model, where participants only need to transmit their locally trained model parameters or gradients without sharing the raw data but ensuring data privacy. However, unlike federated learning in image or text classification tasks, graph-structured data exhibits complex topological relationships, which impacts the convergence of federated learning. In this study, we introduced a model aggregation algorithm for federated graph learning, named FedGda. We assigned different weights to models with different clients based on the density of subgraph connections and denser subgraphs with higher weights to the models. FedGda employs the structural information of subgraphs to converge the global model. The proposed approach effectively combined other federated graph learning methods. We experimented with Independent and identically distributed (IID) and Non-Independently and Identically Distributed (Non-IID) environments, and the result demonstrated the effectiveness of the proposed method. Code is available at https://github.com/xianglinllllll/renxiang.
Edukasi Kemampuan Geospasial Badan Pengawas Tenaga Nuklir

Background: The Nuclear Energy Supervisory Agency (BAPETEN) is responsible for supervising nuclear utilization in Indonesia in collaboration with Linkeu Regional Government in a Geospatial learning process. This activity aims to prepare a base map and land capability analysis map for constructing a Nuclear Power Plant. Method: The training activity ran two days (2-3 August 2023) in Jakarta with Arc GIS 10.8 Software. The map used in making the base map is the Madiun Regency Regional Map, while the analysis map uses the Kendal Regency area. Results: Participants were able to create a base map with the target base map layout according to cartographic rules. Participants prepared land suitability analysis maps based on criteria from several thematic maps using the Weighted Overlay process. Conclusion: This activity produces base and land suitability maps that fulfill cartographic aspects. Participants understand the mapping process, proven by the post-test score above 80, and participant satisfaction with this activity is 100%.
PENGARUH METODE MODELLING TERHADAP KEMAMPUAN BAHASA RESEPTIF PADA ANAK GANGGUAN PENDENGARAN UMUR TUJUH SAMPAI SEPULUH TAHUN DI SLB B YAYASAN REHABILITASI ANAK TUNA RUNGU WICARA

Not all hearing impaired children can understand speech just by reading lips. That way, it requires the right way to improve receptive language skills, one of which is using the modeling method. By using the modeling method, it is hoped that children will more easily understand what other people are saying.The aim of this study was to determine the effect of the modeling method on receptive language skills in children with hearing loss aged seven to ten years.&#x0D; The type of research used is quantitative with a one-group pre and post test only design. The data collection technique uses total sampling with a sample of 8 respondents. The research instrument used was ROWPVT (Receptive One-Word Picture Vocabulary Test) and the modeling method module.&#x0D; The results of the pair t-test test obtained a significance of 0.000 which stated that there was an influence of the modeling method in improving receptive language skills in children with hearing loss. The correlation coefficient (r) obtained a result of 0.996 indicating a very strong influence between the modeling method and receptive language. Based on data analysis, the modeling method has an effect on improving receptive language skills in children with hearing loss.
Real-time Health Monitoring and Speedy Personal Alert Support System using Medical IoT-Cloud Infrastructures

Health care monitoring technology is evolving day by day with the help of computational techniques and intelligent diagnosis models. Compared to conventional approaches and clinical observations, recent health care methodologies are heavily depending on more accurate computational platforms to diagnose various diseases. On the stream, computational principles and intelligent diagnosis model are proposed for monitoring and classifying the biosensor signal data. The existing diagnosis procedures that are using crucial computational models have been trained to observe the medical symptoms of particular disease. The contributions of recent medical support systems and computational models are closely made of Internet of Medical Things (IoMT) environment. However, the existing techniques are applied to obtain accurate decisions only rather than speedy response properties. Against the issue, the proposed Real-time Gated Recurrent Unit (GRU) based Health Monitoring (RGHM) system has been initiated to build more reliable, fast and accurate health assistance solutions. Most of the recent works are taking more complex data processing techniques that are producing higher delay and resources. The proposed RGHM works against these issues and attains effective results compared to current techniques such as Smart Artificial Intelligence based Monitoring Technique (SAIMT), Patient Monitoring with IoT model (PM-IoT) and Patient Centric Cloud Health Monitoring system (PCCHM). The performance of proposed RGHM is illustrated in result section that shows the RGHM has attained 11% to 14% of better performance than existing techniques.
A Blockchain-Enabled Machine Learning Mask Detection method for Prevention of Pandemic Diseases

During the COVID-19 pandemic, finding effective methods to prevent the spread of infectious diseases has become critical. One important measure for reducing the transmission of airborne viruses is wearing face masks but enforcing mask-wearing regulations can be difficult in many settings. Real-time and accurate monitoring of mask usage is needed to address this challenge. To do so, we propose a method for mask detection using a convolutional neural network (CNN) and blockchain technology. Our system involves training a CNN model on a dataset of images of people with and without masks and then deploying it on IoT-enabled devices for real-time monitoring. The use of blockchain technology ensures the security and privacy of the data and enables the efficient sharing of resources among network participants. Our proposed system achieved 99% accuracy through CNN training and was transformed into a blockchain-enabled network mechanism with QR validation of every node for authentication. This approach has the potential to be an effective tool for promoting compliance with mask-wearing regulations and reducing the risk of infection. We present a framework for implementing this technique and discuss its potential benefits and challenges
W-rank: A keyphrase extraction method for webpage based on linguistics and DOM-base features

This paper addresses the problem of an automatic keyphrase extraction for a webpage text. Our method is unsupervised, and we call it W-rank. In our method, first we extract the text of a webpage and tokenize into three different candidate words list: unigram ,bigrams and noun phrases. Then we assign score to all words based on their individual appearance in linguistic and DOM-based feature sets. In the final step, we rank these candidate words using score and select top 5 keyphrase from each list and combine them as a final keyphrases for a given webpage. We focus more on the relevancy of keyphrases to its content using linguistic features. We compare our method with other methods using precision, recall and f-score. The experimental result shows, W-rank improves the performance of our previous method D-rank and outperforms other state of art methods.
Identification of an Optimized Google PageSpeed Audit-Rule-Sequence to Optimize Page Speed

World Wide Web is a collection of online resources and websites including e-commerce, social sites, educational content, etc. To find relevant online resources, people search these by using search engines by providing their desired keywords. After filtering those keywords, search engine list the most relevant websites which are more optimized and efficient in terms of loading speed. Search engine optimization is a set of techniques used to make a website optimized and relevant to those keywords, and set the rank of a website. An online resource or website will be on the top of the search result set if it has a higher rank in search engines. Page speed is one of the most important on-page search engine optimization techniques that is used to make web site efficient in load time, so the user will get the content of the websites in a minimum time. Google has set page speed as the main factor in a higher ranking in search engines. Getting higher page speed is not an easy task, as several performance matrices must be optimized to get efficient loading time. There are many audit rules which are irrelevant or have less impact on the performance score. So selection of audit rules to be optimized is one of the main decisions before starting page speed optimization work. It will waste of time to investigate audit rules for their impact on performance scores. In this paper, we have analyzed all of the audit rules and identified the most important and relevant audit rules in optimizing page speed. A tool is used to generate the best sequence of relevant audit rules based on weighted performance benefit scores in the execution of each audit rule. The same audit rule sequence is applied on five different websites and is found more than 80% improvement in performance scores by applying the first three audit rules only and above 90% performance score obtained by using the first five to seven audit rules in our proposed audit rules sequence.