Reviewerly/exHarmony huggingface dataset:
Knowledge Graph for Fraud Detection: Case of Fraudulent Transactions Detection in Kenyan SACCOs

Detecting fraudulent transactions in SACCOs is essential in preventing financial losses and maintaining customer. Many SACCOs incur massive financial losses due to fraudulent activities such as corruption, asset misappropriation and fraudulent financial statements. In response to these challenges, we propose an approach that detects and prevents transaction risk by leveraging knowledge graphs which contain connectivity patterns and relations; combined with rules that are exploited to discover the knowledge between the type of transaction and customer thereby detecting any anomalies. The effectiveness of the approach is evaluated using real-world SACCO transaction data and shows that it can detect potential fraud in real-time or near real-time thereby saving funds that would have been lost.
Retelling as a Strategy in Enhancing Students’ Reading Comprehension of Recount Text; A Classroom Action Research

The goal of this study is to improve students' reading comprehension of recount text. It was implemented in the second grade of MTs Bustanul Ulum in Pekanbaru. This study included 30 students who had difficulty understanding recount text. As a result, the majority of them did not meet the English subject's minimum criteria. It seeks to determine whether the Retelling strategy can improve students' reading comprehension of recount texts, as well as what factors influence it. This was a two-cycle action research study. Each cycle included four meetings. The researcher and collaborator worked together to collect data through observation, field notes, tasks, tests, and interviews. The study discovered that implementing a retelling strategy could improve students' reading comprehension of recount text. This conclusion was reached based on an increase in the students' mean score in recount text reading comprehension tests from cycle I to cycle II. Furthermore, it was discovered that the material provided, the teacher's ability to guide and explain the material well, the students' motivation, and the appropriate strategy all influenced the change in students' reading comprehension of recount text. To summarize, the implementation of a retelling strategy improves second-year students' reading comprehension of recount text at MTs Bustanul Ulum, Pekanbaru
A Privacy-Preserving Technique to Identify the Useful Content of Documents Owned by Multiple Institutes

Documents must contain the content needed to ensure effective asynchronous communication among remote users. To identify what should be included in documents, it is useful to centralize and analyze data. However, if complex data combinations can reveal sensitive individual information, data sharing becomes challenging. This study proposes a method that realizes feature selections using multiple regression analyses without sharing the original data by sharing dimensionality-reduced data instead of the original information. The proposed method was demonstrated to select those features that significantly increased the documents’ usefulness based on textual feedback documents for amateur musicians. The proposed method estimated almost the same p-values and linear model parameters for almost all features as when the original data was shared. The proposed method contributes widely to studies of digital libraries, in that it can be applied to any data type.
Appraisal of Paradise: A Sentiment Analysis Dataset of French Tourists’ Experiences in French Polynesia

Sentiment analysis is a significant task within the Natural Language Processing (NLP) field. It aims to identify the sentiment polarity (positive, negative, or neutral) within textual data. This paper presents the development and annotation of a specialized dataset, unprecedented in its focus on French Polynesian tourism. This corpus is composed of tourists’ comments written in French concerning their experience in French Polynesia. The collated dataset comprises 1,592 comments and 10,729 sentiment-annotated triplets (aspect, opinion, sentiment), extracted from these comments. Since the issue of overlapping triplets - i.e., instances where triplets within the same comment share aspects or opinions - emerged during construction, we partitioned the original dataset into two distinct subsets: the first consists of 467 comments (2,235 triplets) devoid of overlap, while the other includes 1,125 comments (8,494 triplets) with at least one overlapping triplet. These three datasets, which enhance the availability of French NLP datasets for sentiment analysis, are accessible on the Hugging Face platform.
MuP-SciDocSum: Leveraging Multi-perspective Peer Review Summaries for Scientific Document Summarization

Scientific article summarization poses a challenge because the interpretability of the article depends on the objective, experience of the reader. Editors/Chairs assign experts in the domain as peer reviewers. These experts often write a summary of the article at the beginning of their reviews which offers a summarized view of their understanding (perspectives) on the given paper. Multiperspective summaries can provide multiple related but distinct perspectives of the reviewers rather than being influenced by a single summary. Here in this work, we propose a method to produce abstractive multiperspective summaries of scientific articles leveraging peer reviews. Our proposed method includes performing extractive summarization to identify the essential parts of the paper by extracting contributing sentences. In the subsequent step, we utilize the extracted pertinent information to condition a transformer-based language model comprising of a single encoder followed by multiple decoders that share weights. Our goal is to train the decoder to not only learn from a single reference summary but also to take into account multiple perspectives when generating the summary during the inference stage. Experimental results show that our approach achieves the best average ROUGE F1 Score, ROUGE-2 F1 Score, and ROUGE-L F1 Score with respect to the comparing systems. We make our code public ( https://github.com/sandeep82945/Muti-percepective-summarization ) for further research.
Leveraging MRC Framework for Research Contribution Patterns Identification in Citation Sentences

Research contributions convey the essence of academic papers, highlighting their novel knowledge and understanding compared to prior research. In this study, we address the challenge of identifying research contribution patterns from citation sentences by leveraging a Machine Reading Comprehension (MRC) framework. The MRC approach formulates the extraction of contribution patterns as a question-answering task, utilizing natural language queries to extract contribution patterns (CONTRIBUTION, INFLUENCE, and FIELD) from the context. Our method outperforms the SOTA NER approach in 2022: W2NER, achieving significant performance improvements of +23.76% and +31.92% in F1 scores for label and entity recognition, respectively. In addition, through manual validation and comparison with ChatGPT annotation results, we demonstrate that the accuracy of our approach is 21.65% higher in identifying research contribution patterns. Moreover, the MRC framework handles nested entities and resolves reference disambiguation more accurately, providing a robust solution for complex citation sentences. Overall, our work presents an advanced approach for identifying research contribution patterns from citation sentences, showcasing its potential to enhance information retrieval and understanding within the scientific community.
Identifying Influential References in Scholarly Papers Using Citation Contexts

Citation count is commonly used as a straightforward metric for measuring the impact of a paper. However, since all citations are treated equally, citation count does not accurately capture the true influence of a particular cited paper on the citing paper. To accurately measure the individual impact of cited papers, it is required to identify those that have a high influence on a citing paper. This paper proposes a method to identify the influential citations using the text of citation contexts, specifically the citing sentences. Citing sentences contain the descriptions of the cited papers and the relationship between the citing paper and each cited paper. The proposed method extracts the descriptions of cited papers from the citing sentences and utilizes them to identify influential references. Experimental results have shown the benefits of using the extracted description of each cited paper.
Advantages of Data Reuse Based on Disciplinary Diversity and Citation Count

The adaption of data sharing is an important process in open science. Prior studies pertaining to data sharing have focused on the data release; however, data reuse is also an important aspect. Although the advantages of data reuse have not yet been demonstrated, they have been expressed in different fields. We selected the most cited datasets in the Data Citation Index – the National Longitudinal Study of Adolescent to Adult Health (Add Health) and the Framingham Cohort – and identified articles that reused them to measure the disciplinary diversity among these articles, as well as advantage in terms of citation counts. Particularly, we identified 2,919 articles that reused the Add Health dataset published in 1,702 resources, indicating high disciplinary diversity. Articles reused that the Framingham Cohort dataset had higher citation counts than those in the journal Circulation from 2004 to 2010. Despite our limited analysis, these results indicate the advantages of data reuse.
The Semantic Mapping of RiC-CM to CIDOC-CRM

This paper investigates the semantic closeness between the Records in Contexts - Conceptual Model (RiC-CM) and the CIDOC Conceptual Reference Model (CIDOC-CRM). The research aims to establish a bridge between these two conceptual models, enabling interoperability and seamless integration of data. In this context, we define a mapping of (1) the main RiC-CM entities (focusing on the record-related entities) to CIDOC-CRM entities, (2) the main RiC-CM attributes to CIDOC-CRM (paths of) properties and entities, and (3) the (mainly, record-related) relations of RiC-CM to CIDOC-CRM (paths of) properties and entities. With this research, we achieve a deeper understanding of the semantic relationship between the two models.
Analyzing Web Search Queries of Before and After Purchase on e-Commerce Site

In this study, we investigated how Web search queries change before and after purchasing a product. We focused on the Web searchers who purchased cameras from an e-commerce site. First, we manually classified the words that characteristically appear during pre-purchase and post-purchase searches with cameras. From the manual classification, we found 14 intents. The intents include model number, evaluation, shipping, and accessory, etc. Based on these classified intents, we then analyzed when the words in each intent were used in queries before/after the purchase. Our analyses revealed that the users narrowed the search space with respect to the product as the user nears purchase.
Embrace Open-environment Machine Learning for Robust AI

Dive into the novel OpenML paradigm, unveiling its transformative approach to robust AI in dynamic environment, shaping Automated Machine Learning with adaptability for ground breaking advancements towards Artificial General Intelligence.
Development of Flap Books as an Alternative for Non-Electronic Science (Biology) Learning Media

Background: Nowadays, utilization of electronic-based learning media (such as internet network, computer, HP) has been significantly increase. However, it has limitations such as electrical disruptions that might occur in schools, unstable internet networks. A teacher has to enforce learning media so that the learning process continues to run attractively and remain meaningful. This study aims to develop a flap book on excretory system material in grade 8 (VIII) SMP / MTs. Methods: The development of this flap book follows Sugiyono's development model. The stages of the model are the identification of potential and problems, data collection, product design, design validation, design revisions, product trials, product revisions, product trials, product revisions. Results: Based on the results obtained from the validation carried out by media experts and material experts, the flap book developed is suitable as a learning media with very good categories. Assessment by teachers received very good results with a percentage of 94.7%. Assessment by students received very good results with a percentage of 91%. Conclusion: Based on the validity test, the flap book deserves to be used as learning media with a very good category. Keywords : Flap book, Science (Biology) Learning Media, Non-electronic
Student's Discipline in Biology Online Learning in SMA Negeri 1 Sindang

Background: In online learning, students are required to have adequate smartphones and the internet, so that many obstacles occur, one of which concerns student discipline, especially in attendance and collection of assignments. The purpose of this study was to determine the discipline of students in online learning that took place at SMA Negeri 1 Sindang Indramayu in biology subjects. Method: The research method used is descriptive qualitative. The subjects of this study were 36 students of class XI Mipa 5 SMA Negeri 1 Sindang. The instruments used were in the form of a daily note sheet and an observation sheet. The data analysis technique used is descriptive qualitative analysis. Results: The results showed that the level of student discipline was high, as seen from the percentage of student attendance on time of 81% and the percentage of student assignment collection on time of 65%. Conclusion: The discipline of students XI mipa 5 in SMA Negeri 1 Sindang during online learning is high
A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis

Objectives: The objective of this study is to introduce an innovative hybrid approach that incorporates CNN and Bi-LSTM models to provide a solution to the sentiment analysis problem. The HCNN-BiLSTM Model is the acronym that we present for this methodology. Methods: Pre-processing, feature extraction, and sentiment classification are the three steps in this procedure. In the pre-processing stage, unneeded data gathered from the source text reviews is filtered out utilizing NLP systems. The prior studies presented an integrated strategy referred to as RBDT, which generates particular feature sets depending on the examination, for effectively extracting features. Next, sentiments are predicted using the proposed cutting-edge HCNN-BiLSTM model and grouped various sentimental phrases into five main groups: interest, sadness, anger, happiness, and disinterest. Findings: The findings showed that in terms of F-measure, accuracy, word count, and computational time, this suggested the HCNN-BiLSTM Model operates better than conventional deep learning (CNN) and machine learning techniques (SVM). Novelty: This proposed approach uses advanced methods on five review datasets, which include the Amazon dataset, Spotify app reviews, FIFA World Cup reviews, COVID-19 Vaccination reviews, and ChatGPT reviews, to produce competitive outcomes. Keywords: Sentiment Analysis, Long Short­Term Memory, Convolutional Neural Network, Natural Language Processing, Machine Learning, Deep Learning
UAV Docking Station: Study on Building an Autonomous Takeoff and Landing Platform for Unmanned Aerial Vehicles

Studies how to increase the efficiency of autonomous operation of drones using an intelligent docking station for unmanned aerial vehicles (UAVs), which improves charging and maintenance, reducing the need for human intervention in these processes. Known examples of designs for an automatic drone recharging system have been considered. The results present a system developed for automatic landing of drones on the platform and concepts for systems for automatically positioning the drone after landing and for wireless charging or replacing the drone battery.
Peran Teknologi Informasi Pada Masa Pandemi Terhadap Kegiatan Belajar Di Sekolah

Information and communication technology is progressing faster and faster, and human activities can complete tasks more quickly and efficiently. Information technology makes it easier for people to communicate, interact and act in various ways. The technology that can use to meet face-to-face needs in large quantities is Zoom Cloud Meeting, Google Meet, and Cisco Webex, and there are several other applications for similar purposes but are less desirable. During a pandemic such as Covid 19 and its various derivatives, since it was known until now, it is a scary thing for the community because the impact arising from contracting this virus can result in death. So from 2020 until early 2022, school learning activities were closed due to the PSBB policy from the government. It will be very detrimental to society, especially for school-age children who still need guidance from teachers in understanding lessons. So we need another policy in its implementation, namely the implementation of online or online learning. The utilization of information technology such as Zoom, Google Meet or Cisco Webex as a face-to-face tool between teachers and students through internet media is essential. This situation also does not escape the State 3 Seluma Senior High School, which also carries out online teaching and learning activities.
Revealing The Advantages of the Best Cooking Oil Brand: A Case Study of Weighted Aggregated Sum Assessment (WASPAS) Method in Decision Support System

The purpose of this research is to implement the Weighted Aggregated Sum Product Assessment (WASPAS) method in determining the best cooking oil brand. The research method used is the Weighted Aggregated Sum Product Assessment (WASPAS) method with the stages of determining criteria, weights, and alternatives, creating a decision matrix, calculating the normalization value of the matrix and WASPAS weights in decision making Calculating the Qi value of normalization and weights makes ranking from the highest Qi value. From the research it is known that alternative A4 with the description Bimoli has the highest Qi value of 0.9162, so it is concluded that alternative A4 or Bimoli was chosen as the best cooking oil among the eight alternatives or other cooking oil brands. The result of this study is that the WASPAS method is able to recommend the selection of the best cooking oil for consumers.
Goods Inventory System Using Visual Basic.Net at PT. Mitra New Grain with Waterfall Method

This research is motivated by the process of making raw material inventory stocks that still use paper and have not been connected to the database. The problems that arise in the company are regarding the stock of goods, the process of reporting incoming and outgoing goods is still by handwriting which results in calculation errors and differences between physical data and record data. The design of this raw material information system uses the Visual Basic.Net programming language and SQL Server as a database. The system development model used is the waterfall model, analysis and design using diagrams contained in UML. While data collection techniques use research methods by means of observation, interviews, and literature studies. The purpose of this research is to design a raw material inventory information system that can support all incoming and outgoing inventory activities in the company. The result of this research is a desktop-based inventory system application that can assist in processing inventory data and reporting data.
Harnessing Item Features to Enhance Recommendation Quality of Collaborative Filtering

Recommendation systems provide ways of directing users to items that may be relevant to them by guiding them to relevant items that will be suitable to the users according to their profiles. Collaborative filtering is one of the most successful and mature techniques of recommender system because of its domain independent ability. Bayesian Personalized Ranking Smart Linear Model (BPRSLIM) is model-based collaborative filtering (CF) recommendation algorithm that usually reconstructs a scanty user-item matrix directly; also, using only user-rating matrix usually prevents the algorithm from accessing relevant information that could enhance its recommendation accuracy. Therefore, this work reconstructs BPRSLIM user-item rating matrix via item feature information in order to improve its performance accuracy. Comprehensive experiments were carried out on a real-world dataset using different evaluation metrics. The performance of the model showed significant improvement in recommendation accuracy when compared with other top-N collaborative filtering-based recommendation algorithms, especially in precision and nDCG with 30.6% and 22.1% respectively.
File Cryptography Optimization Based on Vigenere Cipher and Advanced Encryption Standard (AES)

The rapid The main problem in the misuse of data used in crime is the result of a lack of file security. This study proposes a data security method to protect document files using the Advanced Encryption Standard (AES) algorithm combined with the Vigenere Cipher. This research carried out 2 processes, namely the encryption process and the decryption process. The encryption process will be carried out by the AES algorithm and then encrypted again with the Vigenere Cipher algorithm. The experiments show that the proposed method can encrypt files properly, where there are changes in the value of the document file and the encrypted file cannot be opened and the description results do not cause changes to the original file. The results of this study are that the system is able to work properly so as to produce file encryption and decryption using the AES method combined with the Vigenere Cipher. In document files, the largest difference in encryption and decryption time is 8 seconds, while in image files the difference in encryption and decryption time is 17 seconds. This longest time difference is generated by large files.
Opinion Mining on Chat GPT based on Twitter Users

The presence of Chatbots can assist humans in their everyday lives. Chat GPT is one of the commonly used Chatbots that humans rely on to support their work, serve as an assistant, or even create artistic works or writings. The purpose of this research is to investigate opinions regarding the presence of Chat GPT. This Opinion Mining method is conducted by crawling data from Twitter, which can be categorized into three opinions: Positive, Negative, or Neutral. To calculate the accuracy level of the model created, two algorithms, Naïve Bayes and K-Nearest Neighbour, are compared. The model validation process utilizes K-Fold Cross Validation by varying the value of k (k=2, k=4, k=6, k=8, and k=10) and different sampling methods, namely Linear, Shuffled, and Stratified, to obtain optimal accuracy values. The research results indicate that the K-Nearest Neighbour Algorithm achieves the highest accuracy value of 92.40%. Based on this comparison, the K-Nearest Neighbour Algorithm is deemed suitable for modeling Opinion Mining of Chat GPT. The distribution of Twitter users' opinion percentages regarding Chat GPT is as follows: Positive 9.4%, Negative 1.4%, and Neutral 89%. Neutral opinions dominate the results of the conducted Opinion Mining.Keyword : chat GPT, opinion mining, twitter