Reviewerly/exHarmony huggingface dataset:
The Evolution of Intellectual Property Rights in the Digital Age

Purpose: The main objective of this study was to investigate the evolution of intellectual property rights in the digital age.&#x0D; Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings revealed that there exists a contextual and methodological gap relating to the evolution of intellectual property rights in the digital age. Preliminary empirical review revealed that that the digital age has significantly impacted IPR across various dimensions. It has not only necessitated the adaptation of legal frameworks but has also introduced novel challenges and opportunities. The prevalence of digital piracy, the need for copyright enforcement mechanisms, the reevaluation of traditional notions of copyright ownership, and the complexities surrounding open access publishing are among the key issues that have emerged as central concerns in the digital age.&#x0D; Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Institutional theory and the Social Contract theory may be used to anchor future studies on the evolution of intellectual property rights. Some of the recommendations drawn from the study include the following: modernizing intellectual property legislation, strengthening cross-border enforcement, promoting digital literacy and copyright education, encouraging voluntary compliance and industry collaboration and adapting alternative intellectual property models.
XBert - A Model for Hate Speech Detection in Vietnamese Text

— In the digital age, social media's pervasive influence has inadvertently escalated the prevalence of hate speech and offensive comments, with alarming implications for mental health. There is increasing evidence indicating a clear correlation between two factors. exposure to such toxic online content and the onset of depression among users, particularly affecting vulnerable groups like content creators and channel owners. Addressing this critical issue, our research introduces XBert, a model for detecting hostile and provocative language in Vietnamese. We propose an approach related to data preprocessing, improved tokenization, and model fine-tuning. We have modified the architecture of the Roberta model, used the EDA technique, and added a dropout parameter to the tokenizer. Our model achieved an accuracy of 99.75% and an F1-Macro score of 98.05%. This is a promising result for a model detecting provocative and hostile language in Vietnamese.
Pengembangan Keberbakatan dan Minat Siswa dalam Konteks Pendidikan

The analysis that we make this time aims to develop students' giftedness, which the term gifted itself can be described by Plato as Men of gold or "golden men", namely someone who has a superior intellectual character, then translated from the English word gifted, which others are giftedness, talented, talented, creative, insightful, genius and precocious. So what is meant by a gifted person is a person who has a high level of intelligence, judging from the scores obtained from intelligence tests. Two approaches to looking at talent, namely one-dimensional and multidimensional, namely the intelligence dimension and various other measures. And gifted models are (a) genetically oriented, (b) cognitive models, (c) achievement-oriented, and (d) systemic models. The development of student giftedness is someone from the chronological age range in the age range of 12-18 years, and is undergoing psychosocial changes related to identity, independence, friendship, sexuality and achievement, and has a general ability or intelligence above average, high creativity, and commitment to duty.
SDR Tabanlı Radar Sınıflandırma Sistemi Üzerine Mini Bir İnceleme: Son Gelişmeler ve Zorluklar

The shift from analog to digital technologies has transformed radio systems, and Software Defined Radio (SDR) has introduced unparalleled flexibility and adaptability by executing operations through software-based approaches. In recent times, the burgeoning popularity of SDR has augmented their applicability within the realm of Electronic Support Measures (ESM), enabling their utilization for the purpose of radar classification. This short review aims to evaluate existing SDR based radar classification systems in the literature. Firstly, an overview of the existing SDR based radar classification systems is provided for the intended purpose. Subsequently, the process of SDR based radar classification is elucidated. Emphasis is then placed on the primary research obstacles encountered in SDR based radar classification. This is succeeded by a concise examination of unresolved matters to direct forthcoming research endeavors. Furthermore, a brief mention is made of the ongoing research studies conducted by the authors.
Empirical Study And Analysis Of Software Bug Localization Approaches Using Deep Learning

Bugs comes in high volume and documenting them properly in a specific format in the bug reports is difficult. Locating these bugs in correctly identified buggy files is a challenging task which needs to be automated. Numerous tools and techniques are proposed by researchers to support developers and testers to detect buggy files and automate the process of bug localization with greater accuracy. Recent research deal with the automation of Bug Localization process by using different techniques and tools. In this paper, we presented a comprehensive review of few papers in the domain of bug localization. This review helps us to know the benchmark datasets that are used in this process of bug localization, the major techniques that are worked upon, the findings and evaluation criteria and the architecture of various models and frameworks developed by researchers to automate the task of bug localization. This paper works on IR and DNN approaches and attempted to improve the previous results of DNNLOC successfully. The optimized version of the model improved the accuracy from 0.815 to 0.969 for enhanced rvsm and 0.83 to 0.971 for enhanced dnn. It is apparent that information retrieval approach and deep learning approach complement together in the domain of bug localization.
Zhivete Model - A Hybrid of V Model and Agile Scrum for Product Development

The New Product Development Process (NPDP) is a systematic approach to product development, consisting of various stages from ideation to commercialization. The Waterfall model and the Iterative (Agile) model are two commonly used approaches to product development, with each having its own strengths and weaknesses. The V-model is a sequential development process used in regulated industries, emphasizing the importance of verification and validation in product development. Although the V-model is well-suited for regulated industries, it can be inflexible and not adaptable to changes in requirements or feedback. Therefore, a more agile model is needed, with continuous testing and validation throughout the development process. In this paper we are Proposing the Zhivete model - a hybrid of V model and Agile Scrum for product development. Offers structured approach with flexibility and adaptation, potentially reducing time and cost. Keyowrds: New Product Development Process, Waterfall model, V-model, Iterative (Agile) model
Decentralized Voting System Using Blockchain Technology

Blockchain verifies security using a distributed mechanism. For several new and developing products and services, blockchain technology is touted as a game-changer. Providing a distributed architecture to manage and sustain an open voting system would be the aim of such a plan. A system called blockchain stores data in a way that makes it difficult or almost impossible to make changes to it. The sharing economy may be benefited by a very wide variety of applications made possible by blockchain technology. That will be used to quantify how blockchain technology may be used to facilitate distributed electronic voting. The suggested solution implements a voting mechanism using the Aadhar API on an Ethereum network on a blockchain platform. Electronic voting is expected to promote openness, and voters are required to lessen dishonesty and irregularities in the voting procedures. Voters will be able to cast their ballots from home by using this technology. The technology seeks to improve voting process transparency and strengthen fraud prevention by supplanting the conventional pen and paper method. Key Words: Blockchain, Distributed General Ledger, Ethereum, Smart Contracts, P2P network, Consensus Algorithm, PoW Algorithm, Cryptography, Voter Privacy, Trustless Environment, Electrical integrity.
Blockchain based Application for Fake Product Detection

This paper aims to study and introduce an innovative solution that combines cryptography and an immutable blockchain system to address the pressing issue of counterfeit products. The application's primary objective is to identify fake products within supply chains by ensuring data integrity, traceability, and transparency throughout the product's journey. By employing advanced cryptographic techniques, the application secures sensitive information and verifies product authenticity while preserving confidential details. Integrating an immutable blockchain ensures an unchangeable and decentralized record, making it exceedingly difficult for malicious entities to manipulate product records. Blockchain technology can verify the validity of a product and provide confidence to consumers regarding its legitimacy, effectively detecting counterfeits. The QR (Quick Response) code or an encrypted unique code, which is a very effective method for determining the uniqueness of the product, will be used by the model in the suggested system. The blockchain that is accessible after scanning the QR code provides data on the product, the maker, and the owner to assist customers in makinginformed decisions. This study provides a technique foridentifying fake products that uses blockchain technology. Keywords — Blockchain, Ethereum, Counterfeit Product, QR code, Solidity, Dapp.
Performance Analysis of Accuracy of Machine Learning Classifier in the Classification of Breast Cancer Disease

The Breast cancer is recognized as a highly prevalent and life-threatening condition affecting women. In various domains, ML techniques have shown to be reliable predictors. In this study, medical data from Wisconsin breast cancer dataset were used to examine and compare supervised machine learning algorithms for predicting Breast diseases. There are several classifiers with varying level of accuracy, which is the research challenge. This paper suggests a method for enhancing the efficiency and precision of four distinct classifiers: Random Forest Tree, K- nearest neighbor (KNN), Logistic Regression, and SVM. To assess the efficacy of different algorithms, the evaluation employed both the AUC score and confusion matrix. The decision to utilize an algorithm is determined by an AUC score exceeding 0.5, which serves as an assessment metric validating the algorithm's efficacy. The employment of a machine learning technique hinges on achieving this threshold AUC score. Among all the machine learning algorithms tested, the Random forest tree attained the highest AUC score in the trial, reaching 1.0.
Development of Infrastructure and Invoices of Web Banking Software Based on Coreless Banking

Today, many transaction-based analysis methods are used in non-core banking to evaluate web banking software based on customer data sets. The evaluation of the data set of the web banking software leads to the choice of canceling the customer's loan or rejecting the customer's request. This factoring work mechanism involves in-depth evaluation of the dataset or customer information. The data mining approach is described as the most common method for analyzing web banking software, focusing on different algorithms, such as infrastructure development. This important process includes the collection, analysis and final credit decision of various factors that are used to evaluate transaction-based programs. These sources are credit application forms, interbank data sharing, ledger data and the most key internal bank data. The scoring method in granting facilities is often known as scoring, which is actually a method of evaluating loan applications. At the same time, archives and records of past micro-facilities are evaluated to identify characteristics that have an important effect in discriminating between good and bad web banking software applications.
Youtube Comment Feature Selection And Classification Using Fused Machine Learning

The exponential rise of internet platforms, notably YouTube, has resulted in a massive volume of user-generated material, including video comments. Understanding audience input and enhancing user experience require analyzing and forecasting the mood of YouTube comments. This work provides a comprehensive method for YouTube comment prediction and sentiment classification that combines feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA). The first stage is to choose the most informative features from a given dataset using RFE, a common approach for doing so. RFE aids in the elimination of unnecessary or redundant features, resulting in enhanced model performance and decreased computing complexity. The Elastic Net Random Forest with Logistic Regression (RF with LR) technique is then used to construct a robust sentiment classification model. Elastic Net regularization combines the advantages of both L1 (Lasso) and L2 (Ridge) regularization, allowing for improved feature selection and management of multicollinearity concerns. By integrating many decision trees, the Random Forest ensemble approach improves the model's predictive potential even more. We employ Principal Component Analysis (PCA) to increase the classification model's effectiveness and solve possible difficulties created by high-dimensional data. PCA decreases the dataset's complexity while retaining its fundamental qualities, resulting in a more manageable and efficient feature space for classification. Finally, we compare the performance of three prominent classifiers on the preprocessed dataset: Linear Support Vector Machine (LSVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Decision Tree (DT). We can select the best-performing model for YouTube comment categorization by comparing these classifiers.
ASSESSING INTERNET LITERACY AMONG STUDENTS IN NPEO 2022: AN ANALYSIS OF KNOWLEDGE AND SKILLS AT AMBON STATE POLYTECHNIC

This study aims to examine the internet literacy level of students in the National Polytechnic English Olympic (NPEO) selection at Ambon State Polytechnic 2022. The researchers are interested in understanding the internet literacy of 30 students focusing on the use of search engines, social media, email, and downloading. The research employed a descriptive qualitative approach and utilized questionnaires to collect data. The data is then analyzed using the capability Maturity Model Theory. The results revealed that the internet literacy level of students in the National Polytechnic English Olympic (NPEO) at Ambon State Polytechnic 2022 was at level 2 with 50 %. This final result related to the self-rating internet proficiency with 48% (at intermediate level), internet usage and internet capability with 86 % (at good category), and knowledge test with 60 % (at Middle level). The results also elucidated several factors that affected internet literacy among the students, such as gender, age, and internet usage experience.
IMPLEMENTING YOUTUBE AS A MEDIUM FOR TODDLERS WITH AUTISTIC SPECTRUM DISORDER IN SECOND LANGUAGE ACQUISITION

Technological advancements are profoundly impacting education, particularly in the selection of learning media. Appropriate utilization of learning media can make learning easier and more effective. Language acquisition in children typically begins when they first begin to speak words independently. However, autistic children may struggle with acquiring new languages and demonstrating school readiness skills such as social, emotional, language, literacy, and executive functioning due to their individual social, communication, and behavioral presentations. This research aims to determine the implementation of YouTube as a medium for toddlers with autistic spectrum disorder in second language acquisition. The method used is qualitative with the instruments used are questionnaire, observation, and interview. The research subject was 5 autistic spectrum disorder (ASD) students at the Smart Kids School. The researchers did a questionnaire with 5 respondents as the parents of kindergarten students. The researchers also interviewed the teachers of the kindergarten. There were 5 questions to answer, which were described based on the respondents’ opinions. The research results show the process of implementing YouTube in English learning for autistic spectrum disorder students is appropriate and advantageous. Because YouTube provides exciting videos for children; they can hear the words spoken and see their physical appearance, which can aid comprehension. The children often imitate the dialogue of the characters they see on screen and their behavior. In addition, this research also showed that it is crucial for parents and teachers to monitor the content children watch on YouTube.
ENGLISH EDUCATION DEPARTMENT STUDENTS’ SPEAKING ANXIETY IN CLASSROOM ACTIVITIES AT UNIVERSITAS MUHAMMADIYAH GRESIK

The global significance of English is undeniable, especially for students. While learning the language is vital for effective communication, practical usage often remains limited to classroom activities. Surprisingly, English education students also face challenges in practical English communication. Anxiety about making errors leads students to revert to their native language during communication. Language anxiety, in the forms of trait, state, and situation-specific speaking anxiety hampers language mastery. Speaking anxiety particularly affects students especially during presentations and discussions. Addressing this anxiety is crucial for enhancing language skills. By acknowledging this issue, a study was conducted among English education department students at Universitas Muhammadiyah Gresik to identify anxiety triggers and understand strategies for effective English communication. Interview was conducted to explore students’ experiences, emotions, and cognitive processes related to speaking anxiety. The findings unveiled speaking anxiety experienced by students in classroom activities, uncovering the complex interplay of trait and state anxiety as well as situation-specific anxiety. Students’ diverse experiences, thoughts, and triggers shaped anxiety levels, revealing the intricate interplay of cognitive, situational, and individual factors in speaking anxiety. From impromptu to rehearsed speeches, students’ experiences of state anxiety fluctuated, while coping strategies like deep breathing and positive self-talk play a crucial role. Effective preparation and cognitive strategies emerged as valuable tools in managing anxiety. The variety of strategies employed reflects students’ adaptive skills in handling anxiety-inducing situations. This study illuminated the intricate aspects of speaking anxiety and the dynamic strategies students use to navigate the speaking anxiety challenges in classroom activities.
DAMAGED IMPACT OF POOR LITERARY ENVIRONMENT ON HUMAN INTELLIGENCE AND LEARNING ABILITY

The article is devoted to the research of factors influencing the success (continuity, sustainability and long-term) of internalization, that is, the transformation of external knowledge into internal knowledge. The process of internalization is the goal of education and the meaning of the educational process. Among the factors contributing to the success of internalization, the most important is a person’s natural sensitivity. It is expressed, among other things, in a person’s increased or decreased sensitivity to the perception of culture as a system and to the assimilation of cultural codes. The research revealed that such an aspect of culture as the literary environment can have a damaging effect on a person’s intelligence and ability to learn if it is impoverished. It can also have a creative effect if the environment is enriched. Reading and understanding literature allows a person to see society not as a conceivable unity that does not exist in reality, but as a collection of people united by a variety of social connections. Society, thus, appears as a network, and its perception forms in a person the systematic thinking necessary for carrying out any educational activity. The research showed that an impoverished literary environment, on the one hand, can be a fact of a person who does not have high cultural sensitivity. On the other hand, it can be a fact of the education system, in which attention to literature is significantly reduced. All this directly affects a person’s ability to accept and assimilate new knowledge. At the end of the research, it was concluded that literature, both written in the native language and translated, enriches the cultural environment in which a person lives. It has a creative effect on a person’s intellect and is a positive factor in his learning. Reading and understanding literary works forms continuous and sustainable systematic and critical thinking.
A New Approach for Roni Based Medical Image Watermarking Using Contourlet Transform

The majority of modern healthcare systems rely their diagnoses on data collected from the internet. The medical imaging and patient health records are transmitted over the network via the internet-enabled healthcare (E-health) applications. In practical medical applications, the primary problem is the security of medical images transmitted over the internet. For the purpose of embedding secret data on medical images without affecting important information, a trustworthy watermarking approach is required. We provide a unique watermarking approach in this research that embeds the watermark data in the medical image's Region of Non- Interest (RONI). The medical image is subjected to a contourlet transform, and RONI is determined using contourlet coefficients. In this work, we demonstrate the embedding and extraction process of medical image watermarking using Contourlet transform. Experiments are carried out on medical images such as MRI and CT images. The performance of the proposed scheme is analyzed using the metric PSNR. The proposed scheme provides an average PSNR value of 47.1604% for MRI images and 57.5452% of PSNR for CT images.
STUDENTS’ PERCEPTION OF LEARNING ENGLISH THROUGH E-LEARNING MOODLE AT SMK KARYA BANGSA SINTANG

The objective of this study is to discover students’ perceptions of using the Learning Management System (LMS) which is Moodle in learning English at SMK Karya Bangsa Sintang. This study uses quantitative research in the form of a survey study to carry out its goal. The original questionnaire used in this study is from Oktaria &amp; Rahmayadevi (2021), modified by Khaerani (2022) which consists of 16 statements with a five-point Likert’s scale. The researcher collected the data by distributing the questionnaire using a google form to the students at SMK Karya Bangsa Sintang. This study will involve 100 students as the respondents from SMK Karya Bangsa Sintang, 51 students from grade XI, and 49 students from grade X. The findings of this study are expected to show a positive view from the students as they frequently use Moodle as their learning tool at school.