Reviewerly/exHarmony huggingface dataset:
Agent-based Fuzzy Hamiltonian Graph Systems with Artificial Intelligence in the Private Region

In the form of a Hamiltonian graph, there is a Hamiltonian cycle. A fuzzy Hamiltonian cycle is one that departs from each of the vertices or nodes precisely once. A lot of research is being done to figure out how many fuzzy Hamiltonian cycles a fuzzy Hamiltonian graph has it becomes more difficult to recall all the many ways that the edges and vertices have been linked as the total number of vertex and edges grows for several capacities, artificial intelligent are going to complement or increase social facilities. The method is indeed simple to quickly determine whether an edge links the two vertexes when graphs are represented by adjacency matrices. This work proposes a new approach for detecting fuzzy Hamilton cycles with Artificial Intelligence (AI) as well as the level that defines the vertices outside a graph g. In order to show the algorithms utilizing the selected travel system of fuzzy graphs with Artificial Intelligence structure have been generated as well.
Automated Decision Support System for Cyberbullying Detection

Cyberbullying has become a prevalent issue in the digital age, posing serious psychological and emotional risks for individuals, particularly among young people. Detecting and addressing cyberbullying incidents in a timely manner is crucial for ensuring a safe online environment. This project proposes the development of an automated decision support system for cyberbullying detection. The objective of this system is to utilize advanced machine learning and natural language processing techniques to automatically analyse online content, such as text messages, social media posts, and comments, to identify potential instances of cyberbullying. The system will employ a combination of lexical analysis, sentiment analysis, and contextual understanding to accurately detect harmful and offensive language patterns associated with cyberbullying. The proposed system will also incorporate a decision support component to provide actionable insights for intervention and support. It will detect the cyber bullying content in social media before posting so the content will be blocked. When a potential cyberbullying incident is detected, the system will generate alerts and provide information to relevant stakeholders, such as parents, educators, or online platform administrators. This will empower them to take appropriate actions, such as providing counselling, implementing disciplinary measures, or intervening to prevent further harm. To ensure the effectiveness and reliability of the system, a comprehensive dataset of cyberbullying instances will be collected and used for training and testing the machine learning models. Ethical considerations, privacy, and data protection will be carefully addressed throughout the project to safeguard the rights and confidentiality of individuals involved. The success of this project will contribute to the development of an automated solution that complements human efforts in combating cyberbullying. By providing timely detection and decision support, the system aims to foster a safer online environment, protect individuals from cyberbullying incidents, and promote positive digital interactions.
Miner Selection in Blockchain using Proof of Artificial Intelligence

The process of miner selection in blockchain networks plays a significant role in maintaining the efficiency and security of the net- work. In blockchain, the consensus algorithm is crucial for miner selection. Considering the downsides of the consensus algorithm (PoW, PoS) this paper employs proof of artificial intelligence (PoAI) over proof of stake and proof of work. This paper proposes an approach for miner selection using the Proof of Artificial Intelligence (PoAI) framework, which leverages Deep Neural Networks (DNNs) to predict efficient miners for blockchain networks. This paper describes the design and implementation of our PoAI that uses DNN algorithm on a bitcoin dataset, is a combination of supervised and unsupervised learning techniques to model the be- havior of miners using suitable features such as age, number of blocks mined, transaction volume and number of transactions. The proposed methodology improves the scalability of the consensus algorithm.
MedUAV: Drone-based Emergency Medical Supply System using Decentralized Autonomous Organization (DAO)

Unmanned Aerial Vehicles (UAV), often referred to as drones, have the potential to revolutionize the healthcare industry. They offer speed and accessibility in delivering medical supplies, making them the superior mode of transport for inaccessible and remote areas. Though drone-based systems are advantageous, there is a need for an end-to-end system that could provide a secure platform to use drones in a flawless manner to perform transactions. Drones are vulnerable to hacking and data interception, which may lead to the exfiltration of collected data by malicious users. In this study, MedUAV, a prototype for a drone-based decentralized autonomous emergency medicine supply system, is proposed. It leverages the security aspects of blockchain and smart contracts. The proposed system is deployed and tested in the Polygon Main network, and significant conclusions about the performance of the system are discussed.
Online Adaptable Trajectory using artificial Neural Networks for Unknown Environment

Walking in unknown environment is complex process. If robots want to move in complex environment, they have to adapt the changes in trajectory due to uncertain circumstances(obstacles and ditches). In this paper, online trajectories are generated for given boundary conditions using neural networks due to it's universal approximation capabilities. Here, NN is used for coefficients calculation which can adapt a change in constraints during tracking. To investigate the idea that suggested trajectory will have continuous velocity and smooth acceleration, the trajectory has been formulated using neural networks for 4, 6, 7 and 9 constraints and compared. A trajectory is trained only in 2.82 seconds for 6 constraints and in 2 minutes 91 seconds for 7 constraints in matlabs on intel Core i5-4200 CPU, 2 Cores, 4 GB RAM laptop. We have tried to find the best trajectory fit in less computation and time. These NN trajectories can adapt the online changes in constraints due to uncertain environment conditions.
Secured Health Data Sharing System using IPFS and Blockchain with Beacon Proxy

Transfer of data over the blockchain is significantly improved over the years. This proposed system gives a unique method of transferring patient health data to another hospital or to a peer, using a blockchain-based web application that places patient ownership and data security at the forefront. Patient data is stored in a decentralized and distributed manner using the Interplanetary File System (IPFS), eliminating the need for a central authority. Smart contracts written in Solidity facilitate transactions by checking for sufficient funds and managing the transaction process. A beacon proxy is used to automatically update patient ownership in the smart contract, reducing errors and inaccuracies resulting from manual intervention. The blockchain network offers a secure and decentralized solution for transferring patient health data, ensuring privacy and accessibility only to authorized individuals through advanced cryptographic techniques, distributed data storage, and smart contracts.
Revolutionizing COVID-19 Management: Block chain-Enabled Prediction and Secure Storage using Deep Learning Techniques

The proposed research presents a blockchain-based healthcare system that seeks to address the limitations of current data-sharing methods by providing secure and efficient data transfer, enhancing data privacy and security, enabling effective data sharing interoperability, and promoting data-driven healthcare decision-making. A blockchain platform, IPFS, and rule-based access control are among the technologies suggested for use in the proposed system. Implementing this system could result in more personalized and effective medication for patients, as well as lower healthcare costs and improved care quality. The suggested approach, known as the Homomorphic Zero-Knowledge Blockchain Algorithm (HZBA), employs a block chain technique linked with Proxy Re Encryption with Homomorphic with Zero proof knowledge. Finally, the Covid19-dataset has collected from Kaggle repository and HCDNN method has used to complete the classification. The proposed blockchain-based healthcare system has the potential to revolutionize healthcare by resolving data privacy and security concerns, increasing data accuracy, fostering more interoperability in data sharing, and enhancing data-driven healthcare decision-making. The proposed blockchain-based healthcare system provides potential answers to data sharing difficulties in the healthcare business. One potential disadvantage is the system's complexity and cost of installation. The experimental results and classification performance parameters have been thoroughly demonstrated
Security Implementation on IoT using CoAP and Elliptical Curve Cryptography

IoT devices typically encompass objects or devices equipped with software and internet connectivity, allowing them to collect data. They have limited processing power, memory, and storage. These limitations can make it difficult to secure these devices, as traditional security measures may not be practical. Implementing security for IoT devices can be challenging due to these characteristics, but several steps can be taken to mitigate these challenges. Elliptic Curve Cryptography (ECC) can secure resource constrained IoT devices by providing a more efficient method of encryption and authentication than traditional methods such as RSA. In this paper, we analyzed the lightweight cryptographic algorithm ECC for securing resource-constrained devices such as IoT and evaluated its performance compared to the RSA cryptosystem. The analysis result indicates that ECC is considered to provide stronger security than RSA for the same key length. The CoAP protocol is applied to devices with limited resources, and an examination of its impact is conducted, considering three key performance metrics: CPU usage, bandwidth efficiency, and message communication latency.
REC CONNECT: A Comprehensive Android File Transfer Application

The REC CONNECT Android app simplifies file sharing among devices by allowing users to exchange files via QR codes or IP addresses. The application was created in Android Studio, which is integrated with Firebase database for performing optimal storage and transfers. Its easy-to-use interface allows users to generate QR codes or enter recipient IP addresses. This adaptability accommodates to a wide range of user preferences and configurations. Receivers initiate quick file transfers by scanning the sender's QR code or utilizing IP addresses, ensuring cross-platform interoperability. The app's functionality is supported by Firebase integration to ensure secure file storage and transfer. The real-time database handles metadata, while Firebase storage safeguards the actual file data. REC CONNECT holds the potential for expansion, with prospects like robust file management, varied format compatibility, and cloud integration. The app's reliability and privacy-concerned design address the data security concerns. The developed REC CONNECT application reshapes file sharing and increases the efficiency, and security.
Advancements in Real-Time Face Recognition Algorithms for Enhanced Smart Video Surveillance

Smart video surveillance systems are essential for guaranteeing security in a variety of settings. The capacity to recognize and follow people of interest inside video feeds is made possible by real-time face recognition, which is a key component of these systems. The refinement of a real-time face recognition system designed for video surveillance applications is the focus of this research. We proposed a technique that can effectively recognize faces in a variety of lighting situations, poses, and occlusions by utilizing innovative combination of Haar Cascade and Convolutional Neural Networks. To balance computational difficulties and increase scalability, the merging of edge computing and cloud-based solutions are also investigated. The performance of the proposed system is evaluated by vigilant evaluation, demonstrating its applicability in real-world circumstances. Our research aims to advance video surveillance technology with an emphasis on the synergy of real-time face recognition for more secure and effective surveillance systems.
Compressed Deepfake Detection using Spatio-Temporal Approach with Model Pruning

Deepfake is a deep learning technology that replaces source person face photos with the target person face photos in a movie to create a video of the target by executing the actions performed by source person. Due to the limited storage capacity and network bandwidth constraints, compressed media is now commonly employed in social networks. The goal of this research study is to determine whether or not a given compressed video is a deepfake. To do this, both the spatial and temporal components of the video should be considered, and the findings will be integrated by using an appropriate fusion approach. Hence, the deep learning model used in the spatial approach is pruned using the Network Pruning technique to achieve a better performance. The combined prediction of the spatial and temporal approaches indicates whether the given video is deepfake or not.
Implementation of the updated Federal State Educational Standards: The native (Tatar) language teaching

The article discusses issues related to the organization of the educational process in the native (Tatar) language in the context of the introduction of the updated Federal State Educational Standards of General Education. A review of methodological literature and federal curriculum plans on academic subjects “Native (Tatar) Language”, “State (Tatar) Language of the Republic of Tatarstan” allowed us to come to the conclusion that in order to achieve personal, meta-subject and subject learning results, modern native language teachers should have certain general pedagogical and methodological skills, be competent in their subject area, as well as in the field of modern pedagogical technologies. In general, the main activity of the Tatar language teacher is to organize collective speech communication and update knowledge based on the creation of a positive psychological climate in the classroom; to assist students in achieving learning outcomes; to use interactive techniques and activity-based types of work; to positively evaluate students’ activities; to stimulate interest in learning their native language; to improve the quality of scheduled and extracurricular work on the subject. It is also important to note that currently the study of the native languages of the peoples of the Russian Federation is one of the priorities in the system of general education, which emphasizes the relevance and significance of this study.
Evaluating LDA and LSA for Topic Modeling in the Indonesian Natural Disaster

Topic Modeling is a method for analyzing topics, documents, and articles in Natural Language Processing. The LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) algorithms are widely used in topic modelling. This study focuses on analyzing articles related to natural disasters in the Indonesian language. The dataset for this study was obtained through data scraping from Google News, which served as a container for several articles and online news sources. The research method is divided into several stages: dataset scraping, data preprocessing, topic modelling with LDA and LSA, model visualization, and model evaluation. The results of the research show that both algorithms can generate topics on datasets relevant to natural disasters in Indonesia such as floods, earthquakes, landslides, tsunamis and so on. From the evaluation results using Coherence Scores, it is found that LDA has better performance than LSA seen from Coherence Scores in modeling topics related to Natural Disasters in Indonesia. The results of this research are expected to provide convenience for information system developers in terms of monitoring natural disasters in Indonesia and make it easier for researchers who want to effectively read a document based on the disaster related in the document.
Analisis Sentimen Ulasan Aplikasi TikTok Shop Seller Center di Google Playstore Menggunakan Algoritma Naive Bayes

In the rapidly developing digital era, users' views on mobile applications are a key factor in the success of an application. Understanding user sentiment can help application developers and management to improve service quality and user satisfaction. One of the social media that is experiencing a revolution is TikTok, a short video sharing platform that presents e-commerce innovations through the TikTok Shop Seller Center. Therefore, sentiment analysis was carried out to find out whether user reviews of the TikTok Shop Seller Center application tended to be positive or negative based on the Naïve Bayes algorithm. The research methodology involves data scrapping, data cleaning, preprocessing (case folding, stopword removing, tokenization, stemming), labeling, TF-IDF, data testing using confusion matrix and visualization using wordcloud. The results of research regarding sentiment analysis of reviews of the TikTok Shop Seller Center application on Google Playstore totaling 5000 data, it was concluded that user reviews were classified as negative with a percentage of 86.3% accuracy value, 83.7% precision value, 94.6% recall value and 88.7% % F1-Score value.
Development of a Web-Based Correspondence Information System to Enhance Administrative Services in Higher Education

The administrative services for higher education have been greatly affected by the COVID-19 pandemic. This has also impacted STT-NF. Services to students, such as handling documents that previously required physical presence on campus, now transitioned to an online method. STT-NF has utilized the G-Suite service to facilitate online administrative services for students. However, G-Suite has its limitations, necessitating the development of a Document Submission Information System. The chosen development method for this system is a website employing the Waterfall methodology. After conducting observations and interviews with relevant stakeholders, we identified five issues that the document submission information system had experienced. There are three actors involved. From the previously identified issues, we derived eight solution analyses. Testing was conducted using the Blackbox method for the 11 document submission information system features. The testing results indicate that all features are functioning as intended and have passed successfully.
Effectiveness of Android-Based Learning Media in Digital Simulation Subjects

The increasingly rapid digital development demands that the learning media used can attract students' interest and attention. The research method used is Research and Development with 4D development stages (Define, Design, Develop, Disseminate). The aim of this research is to determine the effectiveness of Android-based learning media in digital simulation subjects at Padang City Vocational Schools. The research population was 73 Padang City Vocational High School students who were randomly selected from SMKN 1 Padang, SMKN 2 Padang, and SMKN 4 Padang. Based on data from the effectiveness test results, the average effectiveness was 84.96%. So it can be concluded that the use of Android-based learning media in digital simulation subjects at the Padang City Vocational High School is categorized as an effective media.
Information Security Integration with Agile Software Development: Systematic Literature Review and Expert Judgement

Many businesses are using the Agile Software Development (ASD) to react to changing needs and provide functional values quickly. Information security, on the other hand, is seen as a non-functional requirement. In consequence, information security is frequently ranked lower than functional needs in ASD. The goal of this study is to present a unified perspective on how researchers integrate information security into agile software development. This study uses Systematic Literature Review (SLR) approach from ACM, IEEE, ScienceDirect, and Scopus. This study identified several key groups related to the integration of information security with ASD: agile methodologies, agile ceremonies, advantages, and issues. According to the findings of this study, all agile ceremonies contain some form of enhancement in process or artifacts linked to security. These findings have also been validated by expert judgment who have experience working in the project team that have integrated information security into agile software development process.
BISINDO Sign Language Recognition: A Systematic Literature Review of Deep Learning Techniques for Image Processing

This study uses the Systematic Literature Review (SLR) method to examine the application of deep learning techniques focusing on BISINDO (Bahasa Isyarat Indonesia) image recognition. This is crucial for enhancing communication accessibility for the hearing-impaired community. The SLR process involves three stages: planning, conducting, and reporting. During the planning stage, research topics, questions, and search criteria are established, while the conducting stage involves comprehensive article retrieval and rigorous filtering. In the reporting stage, the study highlights the significance of various deep learning methodologies, including the implementation of several algorithms that ace in image recognition. For example, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and a combination of the two methods, each with unique advantages and limitations. Along with that, this paper aims to find gaps in previous research and act as a guide for future deep learning model development. Moreover, the research outlines the development of a high-performance model, emphasizing key phases such as image augmentation and data preprocessing, as well as model optimization. These efforts contribute to a better understanding of BISINDO image recognition, offering valuable insights for researchers and practitioners aiming to support easier accessibility and communication for the hearing-impaired community through advanced deep learning approaches.
The internal logic and finite colimits

We describe how finite colimits can be described using the internal language, also known as the Mitchell-Benabou language, of a topos, provided the topos admits countably infinite colimits. This description is based on the set theoretic definitions of colimits and coequalizers, however the translation is not direct due to the differences between set theory and the internal language. These differences are described as internal versus external. Solutions to the hurdles which thus arise are given.