Reviewerly/exHarmony huggingface dataset:
Breast tumor prediction and feature importance score finding using machine learning algorithms

The subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process. The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes.
application of Kahoot as an online assessment tool to foster student’s engagement: Student’s experiences and voices

The advancement of technology has an impact on numerous aspects, including assessment. Students tend to feel bored, nervous, and anxious while doing the test. Consequently, the educators are required to create a meaningful atmosphere in the classroom. Kahoot can be used as an alternative assessment tool. The purpose of this study was to gain knowledge about the students’ voices and experiences with the use of Kahoot as an assessment tool in their English grammar class at the end of each of the fourteen meetings. This research was conducted at a private university in Riau with seventy-five participants in the English language education study program. This research was descriptive-qualitative by applying a questionnaire and interview. The findings show that most of the respondents are giving positive responses to using Kahoot as an assessment tool. The result of the research revealed that Kahoot is more preferred by the students than a paper-based test, which offers challenges, an interactive interface, sound, and music, helps students focus on the subject, and increases their interest and motivation. Meanwhile, the researcher found that the weaknesses of Kahoot are the time limit given and the unstable internet connection. Overall, the researcher suggests employing Kahoot as an assessment tool to boost students’ engagement.
Formal model of multi-agent architecture of a software system based on knowledge interpretation

The use of agents across diverse domains within computer science and artificial intelligence is experiencing a notable surge in response to the imperatives of adaptability, efficiency, and scalability. The subject of this study is the application of formal methods to furnish a framework for knowledge interpretation with a specific focus on the agent-based paradigm in software engineering. This study aims to advance a formal approach to knowledge interpretation by leveraging the agent-based paradigm. The objectives are as follows: 1) to examine the current state of the agent-based paradigm in software engineering; 2) to describe the basic concepts of the knowledge interpretation approach; 3) to study the general structure of the rule extraction task; 4) to develop the reference structure of knowledge interpretation; 5) to develop a multi-agent system architecture; 6) and to discuss the research results. This study employs formal methods, including the use of closed path rules and predicate logic. Specifically, the integration of closed path rules contributes to the extraction and explication of facts from extensive knowledge bases. The obtained results encompass the following: 1) a rule mining approach grounded in closed path rules and tailored for processing extensive datasets; 2) a formalization of relevance that facilitates the scrutiny and automated exclusion of irrelevant fragments from the explanatory framework; and 3) the realization of a multi-agent system predicated on the synergy among five distinct types of agents, dedicated to rule extraction and the interpretation of acquired knowledge. This paper provides an example of the application of the proposed formal tenets, demonstrating their practical context. The conclusion underscores that the agent-based paradigm, with its emphasis on decentralized and autonomous entities, presents an innovative framework for handling the intricacies of knowledge processing. It extends to the retrieval of facts and rules. By distributing functions across multiple agents, the framework offers a dynamic and scalable solution to effectively interpret vast knowledge repositories. This approach is particularly valuable in scenarios where traditional methods may struggle to cope with the volume and complexity of information.
REVIEW OF EXISTING BARCODE SOLUTIONS IN BIOMETRICS AND ITS APPLICATIONS BASED ON QR CODES

In this study, a comprehensive analysis of existing solutions for encoding barcodes in the field of biometrics is presented, with a special emphasis on integration into QR codes. The research begins with an introduction to the analysis topic, highlighting the critical intersection of biometrics and QR code technology. The main objective is to assess potential areas of application and the advantages of using QR codes as a means of encoding biometric data. The study underscores the importance of this integration in strengthening security measures, optimizing identification processes, and expanding the scope of application in various domains. Additionally, the study describes the methodology employed, including a detailed review of relevant literature and a general analysis of existing solutions. The results of this research shed light on the effectiveness and adaptability of biometric QR codes, providing valuable insights into their practical application. This study makes a significant contribution to the field of biometric technology, illuminating the innovative use of QR codes. The research findings serve as a valuable resource for both academic circles and practical implementation, offering a foundation for further refinement of secure identification methods.
NFT-ovi I AUTORSKA PRAVA – POSEBAN OSVRT NA TZV. “UMJETNIČKE NFT-ove”

The paper deals with the phenomenon of non-fungible tokens (NFT), and its particular focus is on the so-called “Art NFTs”, and on the legal demystification of the superior attributes assigned to these tokens. The paper addresses a number of issues. Firstly, it examines the legal and technical possibility to actually create a “digitally unique” piece of author’s work. Secondly, it explores the acquisition of ownership and copyright in the work of art, to which the NFT referres to, during the process of creation and transfer of that NFT. Finally, the paper scrutinizes the topics of copyright violations, which may occur while minting the tokens and of the destruction of the physical original of the tokenized work. The paper also deals with two relevant cases from practice (Miramax, LLC v. Tarantino and Free Comb With Pagoda, J.M. Basquiat), which illustrate how the so-called “Art NFTs” collide with copyright principles.
Klasifikasi Status Hipertensi Pasien UPTD Puskesmas Sempaja, Kota Samarinda Menggunakan Metode K-Nearest Neighbor

Data mining is a method of selecting, exploring and modeling large amount of data to find knowledge and clear patterns or interesting relation of the data and useful in the process of data analysis. In data mining there are several techniques that have different function and one of them is classification tehcnique. The classification process itself is the process of finding patterns or differences between classes or data that can be used to predict object classes whose class labels are unknown. K-nearest neighbor (K-NN) is one of the methods in classification algorithm. This study discusses the classification using K-NN algorithm which is applied to the data hypertension status. The aim is to find out the optimal neighborliness value (K) accuracy value and the best propotion of the data hypertension status. The data used is the data of patients UPTD health center Sempaja, Samarinda city from February to May 2022 with dependent variabel is hypertension status and uses 4 independent variables, age, gender, diabetes mellitus and heart disease. Based on the research that has been done, obtained an accuracy value of 62,60% with K = 5 in the best proportion of the data is 70%:30%.&#x0D;
Penerapan Metode K-Means Dalam Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator Pendidikan

Cluster analysis is an analysis that aims to classify data based on the similarity of spesific characteristics. Based on the structure, cluster analysis is divided into two, namely hierarchical and non-hierarchical methods. One of the non-hierarchical methods used in this study is K-Means. K-Means is a partition-based non-hierarchical data grouping method. This purpose of this study is to obtain the best results of grouping regencies/cities on the island of Kalimantan based on education indicators using the K-Means method based on the smallest ratio of standard deviation. Based on the results of the analysis, it can be concluded that the best grouping results based on the smallest ratio of standard deviation is 0.6052 which produces optimal clusters of 2 clusters with the first cluster consisting of 14 Regencies/Cities while the second cluster consists of 42 Regencies/Cities on Kalimantan Island
Statistical Evaluation of Classification Models for PCA Detection

A successful strategy to eavesdrop on the information exchange of legitimate users is by implementing a pilot contamination attack (PCA) during the process of channel estimation. Detection of such an intrusion can be accomplished at the BS and two promising methods that can be used for this purpose are 2-N-PSK and Shifted 2-N-PSK. As binary classification models, in this paper, the performance of both methods can be best evaluated through several commonly used statistical metrics, including statistical accuracy, precision and recall, specificity, fall-out and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score. Other useful information can be obtained from the Receiver Operating Characteristic (ROC) curve and the ROC Area Under Curve (AUC) that represent the trade-off between fall-out and recall. For the purpose of comparing the performance of 2-N-PSK and Shifted 2-N-PSK, in this paper each of the aforementioned metrics is extensively studied, and simulation results are provided and analyzed.
Design of Decision Support Systems with Neural Networks in Biomedical Data Analysis

In this paper an approach is presented for the design of decision support systems with elements of Artificial Intelligence based on neural network classification of toxicological data. Precision and reliability are the top requirements of healthcare systems. The components of the DSS are discussed in detail as well as the possible workflows that will conform to current practices. The application of an $\mathrm{AI} / \mathrm{ML}$ component as well as system improvement propositions on new data is discussed. Experimental results are provided from training and evaluation of toxicological data.
A Graph Based Approach for Cyberbullying Classification Using Machine Learning Algorithms

Using Social Network Services becomes a huge necessary, especially for teenagers. They can get closer in the real time. With more than 237 million daily active users [1], Twitter allows sharing short messages named « tweets ». Some users can employ offensive words, pictures or videos against another person via electronic devices. This phenomenon is calling cyberbullying. Because of timid, fear or weak cyberbullying attacks teens in darkness. Victims suffer in silence and can be driven to lower self-esteem, depression or suicide. In that aims many researches in computer science, sociology and psychiatry take the challenge and many initiatives are developed in order to detect offensive, abusive and negative comments.Because of the huge number of users, the tweets make a raw dataset containing a lot of information that must be purified. Some information are misspelled, missing, duplicated or inuseless. Our work is based on Natural Language Processing; we will discuss the importance of the data pre-processing and feature engineering before proceeding to the text classification using Machine Learning Algorithms.
Arabic stems are being built in a database utilising an automated process.

This study explores the use of machine learning methods to improve morphological analysis of the Arabic language. In order to create a database of Arabic stems that academics can utilize, it examines the most well-known techniques and introduces a novel tool for finding Arabic stems from a corpus made up entirely of Arabic words.We used a corpus of 30 million Arabic words to create this database. From this corpus of words, we were able to attain a precision of 94% using our stem detection method. The benchmarking strategy and these in-depth experimental findings offer a strong basis for assessing how well our novel strategy performs.
An intelligent ANFIS mobile robot controller Using an expertise-based guidance technique

Nowadays, the navigation of mobile robots is becoming a very attractive research problem in the field of robotics to meet the needs of other domains such as medicine, military, space, agronomy, etc. In this paper, we have worked on the reactive navigation of the three-Wheel Differential-Drive Mobile Robot in previously unknown environments, using a minimum of sensors that provide local information about the workspace. Actually, this work consists in implementing an ANFIS-based reactive navigation controller that is trained on a dataset created using an expertise-based guidance technique. Through illustrative simulation experiments, the effectiveness of the proposed controller for navigation is demonstrated with consideration of the three reactive navigation behaviors which are obstacle-avoidance, wall-following, and target-seeking.
Genetic Algorithm-Based Approach for Discovering Involutory MDS Matrices

In this paper, we present an innovative approach for the discovery of involutory Maximum Distance Separable (MDS) matrices over finite fields F2q , derived from MDS self-dual codes, by employing a technique based on genetic algorithms. The significance of involutory MDS matrices lies in their unique properties, making them valuable in various applications, particularly in coding theory and cryptography. We propose a genetic algorithm-based method that efficiently searches for involutory MDS matrices, ensuring their self-duality and maximization of distances between code words. By leveraging the genetic algorithm's ability to evolve solutions over generations, our approach automates the process of identifying optimal involutory MDS matrices. Through comprehensive experiments, we demonstrate the effectiveness our method. The proposed approach opens up new possibilities for practical applications, as well as potential extensions to related problems in coding theory and cryptographic systems.
Comparative Study between Global and Local Explainable Models

Despite opaque machine learning models outperform transparent models, users are still unable to comprehend and trust their outcomes.Therefore, Explainable Artificial Intelligence (XAI) becomes a prominent research area. The main objective of this discipline is providing new techniques, tools and models that explain how opaque machine learning models operate to give predictions or simply offer some information about system decisions.In this article, we present an experimental evaluation of explainable models used in XAI, namely: LIME, SHAP, ANCHOR and EBM. Those models are applied on the results of two opaque machine learning models: Random Forest and XGBoost. Our experimental evaluation covers various aspects for comparison including:•Examining the explainability coverage of methods.•Comparing explanations provided by each method.
Constrained Probabilistic Self-Organizing Maps

Probabilistic self-organizing maps and Gaussian mixture models represent flexible and interpretable probabilistic models that address various machine learning needs, such as speech processing and compression. In this context, the probabilistic self-organization map (PRSOM), as an extension of the classical Kohonen self-organization map (SOM), estimates the density distributions of the data using a combination of normal distributions. However, the likelihood function in the normal mixture may exhibit unbounded features and spurious local maxima (degeneracy). To tackle this problem, we introduce specific constraints to the PRSOM model, based on Ingrassia’s approach. Finally, we provide an implementation of the proposed method and give a comparative evaluation of its performance through numerical experiments.
Deep Learning Empowered Hand Gesture Recognition: using YOLO Techniques

Hand gesture recognition has emerged as a pivotal research domain, particularly within human-computer interaction and assistive technology. This paper presents a comprehensive overview of our study, focusing on the role of YOLO (You Only Look Once) in revolutionizing real-time hand gesture recognition. We delve into the significance of data preprocessing, demonstrating its critical influence on model training and testing. Leveraging a YOLO configuration, we assembled a diverse dataset of over 5000 images, meticulously labeled using the Roboflow platform. This dataset was strategically partitioned into training, validation, and testing subsets, setting the stage for robust model evaluation.YOLO’s object detection prowess, characterized by remarkable accuracy and speed, has propelled it to the forefront of deep learning models. Dividing input images into a grid of cells, YOLO predicts bounding boxes and class probabilities, enabling the concurrent detection of multiple objects in a single pass. This unique mechanism makes YOLO ideally suited for real-time applications, exemplified by its application in hand gesture recognition.Our research showcases the adaptability and customization potential of YOLO, enabling fine-tuning for specific tasks and datasets. By harnessing YOLOv5, YOLOv6, and YOLO v8 models, we conduct an in-depth performance comparison, culminating in the endorsement of YOLOv8l for its superior accuracy and speed. As ongoing research and development continue, YOLO-based models are poised to further refine accuracy and efficiency, solidifying their role as catalysts for improved human-computer interaction and assistive technology.
Enhancing the Traveling Salesman Problem Solutions with Reinforcement Learning: A Variant Exploration-Exploitation Approach Beyond ε-Greedy

In light of significant advancements in the field of artificial intelligence, the traveling salesman problem (TSP) has emerged as a pivotal challenge for AI researchers. This paper endeavors to harness the strengths of reinforcement learning in addressing the TSP, introducing an alternative to the traditional exploration-exploitation dilemma through a variant of the ε-greedy strategy. We tested our approach on standard TSP instances from the TSPLIB dataset, employing both the Q-learning and SARSA algorithms for our experiments. Our findings reveal that our alternative exploration-exploitation method markedly outperforms the conventional ε-greedy approach, showcasing a notable increase in speed and efficacy.
The Development of Learning Videos Using Wondershare Filmora Software on Human Reproductive System Material

The Covid-19 pandemic has changed the way people live, including in the field of education. One of them is WFH (work from home), which causes the learning process to be carried out from home by online. Students found it difficult to understand the material during online learning which caused learning outcomes to decrease. On the other hand, the development of technology and information can be used so that it is beneficial for education. The advantages of learning video media greatly assist the teacher in conveying the material to be delivered and students better understand the material presented by the teacher. For this reason, it was necessary to develop video-based media for learning the Reproductive System material. The purpose of this study was to analyze the validity, feasibility, and influence of media effectiveness on student learning outcomes based on learning videos on the Reproductive System material. The research steps that were passed used the 4D development model, namely Define, Design, Development, and Disseminate. The research instruments used were student questionnaire sheets, interview sheets, validity sheets, teacher and student response questionnaire sheets, as well as pre-test/post-test sheets. The results of research on the Reproductive System learning video media have met the criteria of being very valid by media experts at 98% and valid by material experts with a score of 72%, the media feasibility score is 94% or very feasible by a questionnaire of teachers and students, and learning video media is effective in improving results student learning on the subject of the Human Reproductive System at SMA Negeri 1 Banjarnegara.
Analysis of cloud services using OWASP security design

Cloud computing is extremely popular model among enterprises. It provides more availability of cloud resources when needed with high scalability and extremely cost-effective model. Security is one of the least understood concepts to provide and access for OWASP applications in cloud computing. Many enterprises especially banking clients are risk-averse and have less cloud adoption rate. In this study, security research in the area of cloud security is reviewed and AWS (Amazon Web Service) is the top provider of cloud computing. Since, we have also demonstrated how it works in the security exploration. This paper's major goal is to make cloud computing security is a key activity and prevent unauthorized access to the apps used in the real world.
A Simplified Policy Modification Framework By External provider For Human Medical Record Sharing

The great flexibility and accessibility of cloud computing make it a good fit for storing and managing Personal Health Records (PHR), which are often large and complex datasets. Personal health data, on the other hand, include incredibly sensitive and private information, which raises serious security and privacy concerns. There should also be a flexible and secure access plan for PHR owners' outsourced data. Although Some business cloud platforms offer symmetric or public key encryption as an add-on feature, these conventional encryption techniques are not well-suited for data outsourcing due to the high overhead of key management and maintenance. We create and put into practise a secure, granular access control system with minimal policy modifications for outsourced PHRs. On top of AES and Cypher Text encryption, our suggested method is constructed. In order to provide comprehensive policy change tracing, a policy versioning system is also presented. In the conclusion, we evaluated the effectiveness of the suggested scheme by analysing its performance.