Reviewerly/exHarmony huggingface dataset:
Adaptive Neighbor Graph Aggregated Graph Attention Network for Heterogeneous Graph Embedding

Graph attention network can generate effective feature embedding by specifying different weights to different nodes. The key of the research on heterogeneous graph embedding is the way to combine its rich structural information with semantic relations to aggregate the neighborhood information. Most of the existing heterogeneous graph representation learning methods guide the selection of neighbors by defining various meta-paths on heterogeneous graphs. However, these models only consider the information contained in the nodes under different paths and ignore the potential semantic relationships of nodes in different neighbor graph structures, which leads to the underutilization of graph structure information. In this article, we propose a novel adaptive framework named Neighbor Graph Aggregated Graph Attention Network (NGGAN) to fully exploit graph topological details in heterogeneous graph, and aggregates their information to obtain an effective embedding. The key idea is to use different levels of sampling methods to define neighborhood, and use neighbor graphs to represent the complex structural interaction between nodes. In this way, the high-order relationship between nodes and the latent semantics of neighbor graphs can be fully explored. Afterward, a hierarchical attention mechanism is applied to adaptively learn the importance of different objects, including node information, path information, and neighbor graph information. Multiple downstream tasks are performed on four real-world heterogeneous graph datasets, and the experimental results demonstrate the effectiveness of NGGAN.
Sentiment Analysis of Consumer Post Covid Utilizing Quick Mining Approaches

Consumers and families are challenged by the Post-COVID-19 to maintain a healthy lifestyle, as unhealthful behaviors raise the mortality risk.In this investigation, we look at how the prevalent Corona virus has affected a wide range of consumer attitudes, convictions, and behavior.A variety of client data has been gathered using sentiment analysis.Additionally, utilizing a variety of quick mining methods, current breakthroughs in machine learning algorithms have enhanced sentiment analysis estimates on lifestyles.While machine learning automates the creation of logical models, a perspective's semantic orientation determines whether it is positive, negative, or neutral.This research focuses on the sentiment analysis of lifestyles utilizing quick mining approaches, classifying their polarity as good, negative, or neutral.To estimate attitudes, machine learning employs methods such as Support Vector Regression and K-means clustering.
Legal Frameworks for Cybersecurity in Nigeria - Adapting The Fourth Industrial Revolution

In this article titled "The Legal Framework for Cybersecurity in Nigeria: Adapting the Fourth Industrial Revolution," the author explores the legal landscape governing cybersecurity in Nigeria in the context of the Fourth Industrial Revolution (Industry 4.0). The Fourth Industrial Revolution is characterized by the integration of digital technologies such as the Internet of Things, cyber-physical systems, and cloud computing, which has created a hyper-connected system. As this revolution progresses, cybersecurity becomes critical to ensuring the security of the digital space. The article emphasizes the need for the legal framework to evolve in line with societal changes brought about by the Fourth Industrial Revolution. Nigeria's legal framework for cybersecurity must adapt to the challenges posed by the rapid integration of digital technologies and cyber-physical systems in our daily lives. A robust and adaptive legal framework is essential to safeguard national security, protect personal data, and foster a secure digital ecosystem conducive to economic growth and technological advancements. This Article also calls out to the stakeholders, policymakers and the Government of Nigeria to take the necessary steps required for signing the Malabo Convention, as Nigeria is yet to sign the African Union Malabo Convention. This is vital in our steps to adapt to the Fourth Industrial Revolution. Keyword: Proactive Approach, Security Challenges, Cloud Migration, Security, Risks, Models Proceedings Citation Format Ibitamuno, P.V. (2023): Legal Frameworks for Cybersecurity in Nigeria - Adapting The Fourth Industrial Revolution. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 97-104. https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P12
Application of the Quantum Learning Model to PGMI Students in Mathematics Learning

The application of learning models that are less effective and innovative can lead to low student learning outcomes, so a learning model is needed that is able to motivate students to improve learning outcomes, especially in mathematics content. This study aims to determine the increase in student learning outcomes who get the Quantum Teaching learning model at the PGMI major in mathematics learning. Quantum Teaching provides the view that students in the learning process are important not just sitting and listening, but students need to be active to find their own knowledge. The method used in this study is a qualitative method with a descriptive approach. Based on research it is known that this quantum teaching learning model can increase motivation and understanding of learning in PGMI students in mathematics lessons.
Implementasi Kriptografi Vigenere Cipher untuk Keamanan Data Informasi Desa

Data security on village information is a very important issue because it contains a lot of village information data. Important data cannot be denied that data can be edited or changed by unauthorized persons. So the data becomes insecure. Therefore, a technique is needed to secure data, namely using the classic Vigenere cipher cryptography technique, to maintain the security and confidentiality of messages or information so that it cannot be read by anyone, a desktop-based application is designed using the Java programming language which is carried out on the Netbeans IDE software to implementing the vigenere cipher algorithm as a technique for securing village information.
Blockchain and Federated Learning Based Integrated Approach for Agricultural Internet of Things

The agriculture industry has undergone considerable changes in recent decades due to technological improvements and the introduction of new technologies such as the Internet of Things (IoT), artificial intelligence (AI), and secure communication protocols. Even in harsh weather, it is now possible to grow plants. Knowledge transfer continues to be a significant barrier to the adoption of AI technologies, specifically in the agriculture sector. The concept of “federated learning” (FL) was developed to safeguard data from various users or devices. FL is adequate for protecting data, but there are still several issues to be concerned about, including single-point failure, model inversion attacks, and data and model poisoning. For this study, a blockchain-enabled, FL-integrated framework is suggested as a solution to these problems. Additionally, the experimental setup and the Ethereum network are used to validate the suggested framework and the blockchain transactions.
Modelling of Students` Personality and Self-concept on their Test-Anxiety towards Measurement and Evaluation course

This study established the most meaningful causal model for explaining the influenceofundergraduate students’ personality and self-concept on their test-anxiety towardsmeasurement and evaluation course. We adopted correlation research design in conductingthe study. The participants consist of 150 final year undergraduate students in the FacultyofEducation drawn using multistage sampling procedure. The instruments titled: Students’Personality Trait Questionnaire (SPTQ); Students’ Self-Concept Questionnaire (SCQ), andStudents Test-Anxiety Questionnaire (STAQ) were used for data collection. Datawereanalyzed using SEM-PLS applying the WarpPls software. Results generated a meaningful causal model for explaining the influence of undergraduate students’ personalityandself-concept on their test anxiety towards measurement and evaluation course. Themeasurement model showed a high level of validity and reliability. Results further showedthat 12 paths had a significant direct influence on the endogenous variables in the model. Most indirect paths showed no significant influence on the endogenous variables. It wasrecommended that university students form positive personality traits and self-concept toreduce their test-anxiety level.
Mobile Learning and English Reading Abilities in Postgraduate Students

English as a foreign language has gained popularity as a means of sustaining education by providing students with the option of learning English for specific objectives. The purpose of this study is to measure the level of English reading skills among doctoral students at a Postgraduate University in Peru, using M-learning. A pre-experimental examination led to the development of a hypothetical deductive research procedure. The process for collecting data involved administering a pre- and post-test. The findings of the posttest indicated that 60% of the students attained the level of remarkable accomplishment, 40% attained the level of expected achievement, and 0% were at the process and beginning level. From the findings collected, it can be concluded that Education Doctorate students were able to improve their English reading skills satisfactorily with M-learning application.
Address Search Correction Using Deep Learning

Most digital map applications allow users to search for addresses. However, implementations using Levenstein distance and string matching methods are only suitable for exact address matching, which did not operate well with ambiguous inputs such as typos or alias addresses. We present a machine-learning approach to the Address Correction problem based on Grammatical Correction and Machine Translation. We build a Sequence-to-Sequence (Seq2Seq) Attention model, using Gated Recurrent Units (GRUs) with different Attention forms (including Dot, General and Concatenate Attention). For comparison, we build a long short-term memory (LSTM) Seq2Seq and a Transformer model. According to our experiments, the GRU Attention obtains the best overall accuracy in different datasets. Based on the proposed architecture, we build an end-to-end pipeline for Address Searching with ambiguous inputs using Beam Search for Vietnamese, United States and Australian addresses.
MEMBER ELECTION DECISION SUPPORT SYSTEM SOUTH SUMATERA PASKIBRAKA USING TOPSIS-PROMETHEE METHOD

Paskibraka is the best young generation selected through various selections to raise and lower the Heritage Flag on Indonesian Independence Day. However, in the enthusiasm of the students to take part, the Dispora of South Sumatra Province still uses a manual assessment system so that several obstacles were found in its implementation. done with Microsoft Excel, as well as a calculation system that can only be used for one period, while this selection is an annual event that is held every time to celebrate Indonesian Independence Day. Therefore we need a way that can help the Dispora of South Sumatra Province in determining the best alternative for paskibraka members. One algorithm that is useful in decision support is Topsis. Topsis is used in the application of values for each criterion and a different range of values. Then using the Promethee method can improve the Topsis method because the Promethee method is used to determine the order of priority in multi-criteria analysis. The data taken by 60 participants were then researched according to predetermined criteria including written test scores, interview tests, health tests, physical fitness, and posture. Produced the best participants according to the system as many as 15 data. The results of the research test have an accuracy of 80%.
Comparison Of Shift Reduce Parsing and Left Corner Parsing Algorithm in Sentence Structure Ambiguity Checker

Indonesian is the official language of the Republic of Indonesia and the language of the Indonesian nation's unity. Although it is often used, there are still errors in the use that are not in accordance with the applicable rules. One type of error is due to ambiguity which can cause misunderstandings in interpreting a word or sentence. Structural ambiguity is a type of ambiguity that occurs when the structure of words in a sentence can be given more than one grammatical structure. Left Corner Parsing and Shift Reduce Parsing are parsing methods used to classify sentence structure ambiguity. This research involves preprocessing, namely case folding, tokenizing and Part Of Speech Tagging. This study uses 90 testing data labeled with facts, 30 ambiguous sentences and 60 unambiguous sentences. Based on the results of checking the ambiguity of the sentence structure, the Shift Reduce Parsing algorithm produces an accuracy of 71%, precision 70.6%, recall 59%, and f-measure 58.2%. Meanwhile, Left Corner Parsing produces an accuracy value of 70%, precision 68.7%, recall 57.5%, and f-measure 55.8%.
Classification of Emotions on Twitter using Emotion Lexicon and Naïve Bayes

Social media is a means of interaction and communication. One of the social media that is often used is Twitter. Twitter allows its users to express many things, one of which is being a personal media to provide various kinds of expressions from its users such as emotions. Users can express their emotions and sentiments through writing on the status of their social media posts. One method to find out the emotion in the sentence is using the Emotion Lexicon. However, the lexicon-based method is not good at classifying data because not every word contains emotion. So, there's a need to combine it with other classification method such as Naive Bayes. Naïve Bayes relies on independent assumptions to obtain a classification through the probability hypothesis that each class has. The results of the classification test with Emotion Lexicon alone have 46% accuracy, 45% precision, 51% recall and 36% f-measure. While the results of the classification test with Emotion Lexicon and Naïve Bayes resulted in an accuracy of 65%, precision of 77%, recall of 55%, and f- measure of 59%.
PENGARUH MEDIA REALIA TERHADAP HASIL BELAJAR IPAS

This research is motivated by several factors, namely: the media provided by the school does not meet the needs of students, the surrounding environment is not maximized, and the lack of media use in materials that should involve media. The formulation of the problem proposed in this study is whether realia media has an impact on student IPAS learning outcomes. The purpose of this study was to determine the effect of realia media on the learning outcomes of IPAS on buying and selling material for grade IV students. This research method is a quantitative approach with the type of experimental research, namely research conducted to test the validity of experimental variables. In this study, researchers sampled the entire student population. The data source of this research comes from a sample of 52 students. Based on the results of hypothesis calculation, the value of Thitung (-19.896) &lt; Ttabel (2.009) is obtained. In accordance with the statistical hypothesis that has been made before the research, it can be concluded that there is an effect of realia media on the learning outcomes of IPAS grade IV students at a significance level of 5% (a = 0.05).
Recognition of Online Turkish Handwriting using Transfer Learning

We present a recognition system for online Turkish handwriting using transfer learning. Training deep networks requires large amounts of data. Since such a sufficiently large collection of Turkish handwriting samples is not available, So we adopt the transfer learning approach and train and optimize a CNN-BLSTM recognition system first using the standard IAM-On dataset of English handwriting. Then, we fine tune it with Turkish handwriting samples from a smaller dataset. Fine tuning increases the character recognition rate of the final system which is evaluated on 2,041 samples of isolated Turkish words from the initial value of 49% to 85%. The results show that transfer learning can be a solution to the data scarcity problem of online Turkish handwriting.
A Comparative Study of Tamil Handwriting to Determine the Sex of an Individual by Analyzing the Class Characteristics

Handwriting analysis is a subset of forensic science that deals with questioned documents. The main objective of handwriting analysis is to analyze, compare and evaluate handwriting characteristics to determine the sex of an individual. The class characteristics that were analyzed in this study are alignment, spacing, slant, line quality, pen pressure, movement, size, and speed of an individual’s handwriting, the primary goal of this study is to determine the sex of an individual through handwriting analysis; thus, the total number of samples 200 in which 100 male and 100 female transitive Tamil handwriting samples were collected in the age group ranges from 20 to 40 on hp A4 sheet without any indentation marks on papers. All individuals are strictly asked to write with Rorito bright max ball pen. With the help of a Scale, Pencil, three folding magnifiers 10x (Micrometer), and a Pro illuminated magnifier 4x the sample was analyzed, and interpreted the class characteristics between males and females. The alignment, spacing, line quality, and speed of males show a significant difference from in females and entered the data in the form coding system. The area of this study is some part of Puducherry. It would be helpful for the document examiner to proceed and take initiative in cases related to the determination of sex in male and female in forensic questioned document examination. By concluding this study, the sex can be identified by analyzing the Tamil handwriting.
The effect of average scores in reading, mathematics and science on innovation and income: A quantitative analysis for a group of countries

The purpose of this paper is to The goal of this study is to empirically examine how innovation and income are affected by PISA scores—average scores in math, science, and reading. The specific objectives are threefold. First, we develop a conceptual model that describes the connection between income, innovation, and educational quality as determined by PISA scores. Second, we perform an empirical analysis to see if educational quality as assessed by the PISA scores has an impact on innovation.Third, we also test whether the PISA scores have a positive effect on income (GDP per capita). Using panel mode, we find that education quality has a significant and positive effect on innovation and income. Particularly high math test scores have been found to be an important subject that increases per capita income and innovation.
Improving Transliteration from Nôm Scripts into Vietnamese National Scripts using Class-based Language Model

Nôm is a writing system of Vietnamese that had been used for almost ten centuries and contains a lot of valuable documents. However, because of their complexity, a Nôm script can be transliterated into one or more of the Vietnamese national scripts (polyphonic), which makes transliteration difficult. In this paper, we propose a method to reduce this complexity by grouping similar words in the statistical machine translation process. Especially, we use class-based language models to enhance higher-level abstract information, making the transliteration model better solve the polyphonic. Our experiments give an improvement of +0.39 BLEU compared to the baseline model, proving the effectiveness of the proposed method.
AI Tools for the Researcher: Streamlining the Scholarly Communications Process

When I hear the phrase "artificial intelligence" (AI), immediately my mind goes to robots and self-driving cars and rovers used for space exploration.In other words, I associate AI with complicated tasks that impact only a few of us.But the reality is that AI is everywhere around us, in ways that we might not even realize.We use digital assistants like Siri and Alexa to help us answer questions, make purchases for us, even to make us laugh (my daughters taught me that Alexa tells jokes).And who hasn't used Waze or Google Maps to get from point A to B? Or how about the social media we engage in daily and the content we are served on those platforms?Even autocorrect on our phones and in our word-processing documents are examples of AI.Thanks to the advancement of data collection and analysis, ever-faster computer processing, and the expansion of connected devices, nearly every industry has been impacted by AI in the past several years.For example, there has been the growth of autonomous cars in transportation, the use of robots working alongside humans in manufacturing, the advancement of diagnosing disease through machine-read imaging, and the automation of reporting and processes in finance (1).The scientific research enterprise is no different.
PENGEMBANGAN MEDIA PUZZLE BERBASIS MAKE A MATCH PEMBELAJARAN BERBAGAI PEKERJAAN KELAS IV SEKOLAH DASAR

This research is motivated by learning in the classroom which still uses the lecture method during the learning process that takes place in the classroom and has not used learning media because of the limitations of learning media in schools. This study aims to develop a Puzzle learning media that is suitable for use for students in grade IV Elementary School 2 Karangmulya Plumbon. This type of research is research and development or what we usually call R&amp;D (Research and Development) by following the procedures from Borg and Gall. The research stages include 1) information gathering 2) media product planning 3) media product development and 4) material expert validation, media. The results of this puzzle learning media research are adjusted to the formulation of the problem as follows 1). The development of puzzle media that is specifically planned for 4 sub-theme 2 themes for various social studies learning work using the Brog and Gall research approach with 4 stages. 2). The validity of the puzzle media which was assessed based on the validity test carried out by the validator, with the average obtained from the validation results of the three material experts were 100%, 100%, and 97.9% included in the "Very Valid" category. The average scores obtained from the three media experts were 86.3%, 100%, and 84% included in the "Very Valid" category. From the validation stages of material experts and media experts, it can be concluded that Puzzle media is suitable for use as a learning medium for fourth grade students learning Various Jobs at SD Negeri 2 Karangmulya Plumbon.&#x0D; Keywords: Media Puzzle, Make A Match Based, Learning Various Jobs.