Reviewerly/exHarmony huggingface dataset:
Profile of ICT Literacy in Science Learning Assisted by Google-Sites for Junior High Students

Authors:
raw_author_name: Imesta Zulfanida Ernest
raw_affiliation_string: Yogyakarta State University
raw_author_name: Suyanta Suyanta
raw_affiliation_string: Universitas Negeri Yogyakarta

Abstract: Education in the era of revolution must utilize the application of ICT in science learning which can refer to 21st- century skills is ICT literacy. The purpose of this study aims to see the effect of Google sites-based science learning on the profile of ICT literacy of junior high school students. The method used is quantitative research by giving post-tests to two different classes that have implemented the learning The two classes consisted of the experiment class and the control class. The sampling technique used was cluster random sampling. The results obtained were then analyzed and discussed descriptively quantitative. The results show that the application of Google sites-based learning can affect the achievement of students' ICT literacy profile by obtaining an average score of high categories in the experiment class and medium category in the control class. Based result of study, there is a significant influence of the use of Google-sited based in science learning on ICT literacy in junior high school students
QR Code Use and Identification Problems in Tourism

Authors:
raw_author_name: József UDVAROS
raw_affiliation_string: Budapest Business University, Budapest, Hungary
raw_author_name: Norbert FORMAN
raw_affiliation_string: Budapest Business University, Budapest, Hungary

Abstract: The integration of technology with tourism increases its efficiency and improves the tourist experience, especially through QR codes and NFC technologies, which are already widely used. The number of research related to digitization and the sustainability of tourism is increasing, and the focus is shifting towards ICT, AR and VR. Mobile services like QR codes are crucial in tourism, improving service availability and user experience. The article analyzes the importance of QR codes and image processing in logistics and tourism. The focus is on the technological foundations, recognition, and decoding methods of QR codes, highlighting the role of artificial intelligence. The article discusses in detail the application of QR codes in tourism, along with technological challenges and development opportunities. It also presents future research directions and the potential effects of QR codes on tourism.&#x0D;
A Systematic Literature Survey on Electronic Health Record System Using Blockchain

Authors:
raw_author_name: Rushikesh Pawar

Abstract: Abstract-Compared with traditional paper-based medical records , Electronic health records (EHRs) are widely used because of their efficiency, secu- rity, and reducing data redundancy.The Electronic Health Record (EHR) system is a major technolog- ical development in healthcare that has completely changed how patient data is collected, stored, and used. Through the use of cutting-edge digital tech- nologies, the EHR system offers a safe, centralized location for all patient data. Healthcare personnel may easily access, edit, and share patient informa- tion thanks to its user-friendly interfaces, which helps them make more informed clinical decisions and provide better treatment.The system developed using the Ethereum network using Ganache. The measured approach is to store patients data and execute functions in a decentralized system using blockchain smart contracts. To further protect pa- tient privacy and adhere to privacy laws, the system places a high priority on data security and uses strong authentication and encryption mechanisms. Keywords-Blockchain, Decentralized, Ethereum, Healthcare, Smartcontacts,
Comparative Analysis of Sentiment in Original and Summarized Tweets: Leveraging Transformer Models for Enhanced NLP Insights

Authors:
raw_author_name: Kandethody Ramachandran

Abstract: This paper investigates the sentiments of Twitter users towards the emergent topic of ChatGPT, leveraging advanced techniques in natural language processing (NLP) and sentiment analysis (SA). Our approach uniquely incorporates a dual setting for sentiment analysis: one analyzes the sentiments of original, full-length tweets, while the other first condenses these tweets into succinct summaries before performing sentiment analysis. By employing this dual approach, we are able to offer a comparative analysis of sentiment assessment pre- and post-text summarization, exploring the accuracy and reliability of the summarized sentiments. Central to our methodology is the application of Transformer models, specifically ProphetNet, which facilitates a deeper and more nuanced understanding of the original text. Unlike traditional methods that rely on keyword extraction and aggregation, our approach generates coherent and contextually rich summaries, providing a novel lens for sentiment analysis. This research contributes to the field by presenting a comprehensive study comparing sentiment analysis outcomes between original texts and their summarized counterparts, and examining the effectiveness of different NLP techniques, namely NLTK and the Transformer-based ProphetNet model. The findings offer valuable insights into the dynamics of sentiment analysis in the context of social media and the efficacy of state-of-the-art NLP technologies in processing complex, real-world data.
Analysis of the Impact of Dataset Quality on Task-Oriented Dialogue Management

Authors:
raw_author_name: Cayetano Guerra-Artal
raw_author_name: Mario Hernández-Tejera

Abstract: Task-oriented dialogue systems have become crucial for users to interact with machines and computers using natural language. One of its key components is the dialogue manager, which guides the conversation towards a good goal for the user by providing the best possible response. Previous works have proposed rule-based systems, reinforcement learning, and supervised learning as solutions for correct dialogue management; in other words, select the best response given input by the user. This work explores the impact of dataset quality on the performance of dialogue managers. We delve into potential errors in popular datasets, such as Multiwoz 2.1 and SGD. For our investigation, we developed a synthetic dialogue generator to regulate the type and magnitude of errors introduced. Our findings suggest that dataset inaccuracies, like mislabeling, might play a significant role in the challenges faced in dialogue management.
Deep Learning based Smart Image Search Engine

Authors:
raw_author_name: Ashish Chakraverti

Abstract: Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Texture Algorithm, in CBIR. As the Internet sees an exponential growth of different data types, the importance of CBIR continues to grow. In order to efficiently retrieve images, solely relying on image features while ignoring metadata is exactly what we need. As such, this paper is a reminder of the need for CBIR in this changing world. They showed that CBIR continues to be quite effective in the age of the Internet. Their proposed model for CBIR, which integrates ResNet-50-based feature extraction, a neural network model trained on different image datasets, and clustering techniques to make retrieval fast, provides a significant improvement in accuracy and efficiency for content-dependent image retrieval. This methodology is likely to be very useful as we work with the increasingly huge data of vision and beyond on the Internet. It provides a good basis for an effective image search and retrieval system
Studi Literatur Mengenai Prediksi Kepuasan GrabFood Menggunakan Machine Learning

Authors:
raw_author_name: Arief Setyanto
raw_affiliation_string: Universitas Amikom Yogyakarta
raw_author_name: Asro Nasiri
raw_affiliation_string: Universitas Amikom Yogyakarta

Abstract: The rapid growth of food delivery applications, such as GrabFood, has revolutionized the way consumers access and enjoy their favorite meals. Ensuring customer satisfaction is crucial for these platforms to maintain their competitive edge and foster customer loyalty. If many customers are not satisfied with the service or anything provided by GrabFood, it is possible that they will leave the application soon or later. This research is aimed to review some literature related to customer satisfaction of the food delivery application with the use of machine learning. From this review, one can have more information and insight on how machine learning can help to find the satisfaction level of the customers. The method which is used in this research is literature review which reviews the collection of literature. Varied literature with different variables has been resulted from this research. Many of them use different methods of machine learning.
A Perceptive Mobile Program to Analyze Finger Posture on the Piano using Machine Learning and Object Detection

Authors:
raw_author_name: Sho Ooi
raw_author_name: Mutsuo Sano

Abstract: People from various professions are involved in the production of anime, including directors, animation directors, character designers, and voice actors/actresses. Specifically, the role of an animation director is of importance in the realm of animation. The animation director serves as the unifying force in shaping the animation's style by meticulously reviewing and redrawing the key animations provided by the key animators. The aim of this study is to develop a redrawing system using GLCIC to reduce the workload on animation directors when redrawing original key animation. Specifically, this study devised a system that employs GLCIC to analyze and learn the distinctive drawing styles of individual animation directors from images of their work and subsequently apply those styles to the conversion process. In the experiment, we asked people whose hobby is drawing to experience the developed system, and conducted a qualitative evaluation using a questionnaire and a quantitative evaluation using KLM analysis. As a result, we found that there were issues with ease of modification and UI. Additionally, the KLM analysis revealed that improving the system could reduce work time by a quarter. In the future, we think to improve the system with the aim of increasing work efficiency.
An Optimized Writing Efficiency Tool with Integrated Gamification and Work Interval Timer for Enhanced Essay Productivity and AI Advice

Authors:
raw_author_name: Summer Shen
raw_author_name: Moddwyn Andaya

Abstract: both productivity and writing quality. Students often struggle with overwhelming tasks, slow progress, and lack of motivation during the writing process. Our proposal combines the Pomodoro Technique, gamification, and an AI-powered writing quality checker to offer a holistic solution [8]. The Pomodoro Technique breaks down tasks, while gamification elements, including a playful chicken character, make writing enjoyable. An AI-based writing quality checker provides real-time feedback on grammar, sentence structure, and clarity [9]. Challenges, such as balancing interactivity and efficiency, were addressed through thoughtful design decisions. Experimentation involved applying the application to various writing scenarios, showcasing its adaptability. Results demonstrated enhanced productivity, improved writing quality, and increased user satisfaction. This comprehensive approach addresses the shortcomings of existing methodologies and provides users with a valuable tool to navigate the challenges of essay writing efficiently and effectively.
Domain Adaptation Regularized LayoutLM with Automatic Domain Discovery from Topic Modelling

Authors:
raw_author_name: Chen Lin
raw_author_name: Yourong Xu
raw_author_name: Hui Su

Abstract: In this paper, we propose using domain adaptation to improve the generalizability and performance of LayoutLM, a pre-trained language model that incorporates layout information of a document image. Our approach uses topic modelling to automatically discover the underlying domains in a document image dataset where domain information is unknown. We evaluate our approach on the challenging RVL-CDIP dataset and demonstrate that it significantly improves the performance of LayoutLM on this dataset. Our approach can be applied to other NLP models to improve their generalization capabilities, making them more applicable in real-world scenarios, where data is often collected from a variety of domains.
Prediksi Diabetes Melitus Tipe-2 Menggunakan Sequential Forward Selection (SFS) Dengan Algoritma Support Vector Machine (SVM)

Authors:
raw_author_name: None Cipta Ramadhani

Abstract: Diabetes is a chronic disease and can cause long-term complications if not handled properly. To prevent this, a machine learning model is needed to predict diabetes with high accuracy. This study aims to see the effect of reducing feature dimensions on model performance and to see the effect of data cleaning on model performance. This study used the Pima Indian Dataset, two models were created with different preprocessing stages. The first model was created without performing data cleansing, and the second model was created by performing data cleansing. After the next preprocessing stage, the number of features that produce the best performance is sought using Sequential Forward Selection and the model is drilled using the Support Vector Machine algorithm. After going through the training stage, the two models will be tested and their performance will be compared. The results showed that reducing the number of features made the model have better performance. And of the two types of models, the model that uses the data cleaning stage shows better performance.
The effect of the family environment, school environment, and community environment on students' independence through distance learning

Authors:
raw_author_name: Ahsanul Khair Asdar
raw_affiliation_string: STABN Sriwijaya Tangerang, Indonesia.
raw_author_name: Bambang Afriadi
raw_affiliation_string: Universitas Islam Syekh-Yusuf Tangerang, Indonesia.

Abstract: This research aimed to investigate the effect of the three education centers on students' independence through distance learning. This study used a quantitative approach using a correlational survey method. This study involved 2263 students from Banten Province, Indonesia, who were selected using random cluster sampling. Research data was collected through independent learning questionnaires and family, school, and community environments. All data were analyzed using multiple linear regression analysis. The results of the descriptive statistical analysis show that the average score of female students' learning independence (96.45) is higher than the average score of male students' learning independence (93.38) through distance learning, and the average junior high school students’ learning independence score (95.81) is higher than the average score of learning independence for high school/equivalent students (93.42) and tertiary institutions (94.21) through distance learning. While the results of inferential statistical testing show that in the implementation of distance learning, (1) the family environment has a significant positive influence on independence learning, (2) the school environment has no significant effect on independence learning, and (3) the community environment has a significant positive effect on learning independence. The findings can have implications for the assessment form in the learning process, which is not only the learning achievement from the content assessment but can also use another form, such as students’ independence, like in these findings.
Digital library: Lecturers' perceptions of facilitating learning resources in the industrial era 4.0

Authors:
raw_author_name: Rusi Rusmiati Aliyyah
raw_affiliation_string: Department of Elementary School Teacher Education, Universitas Djuanda, Bogor, Indonesia.
raw_author_name: None Widyasari
raw_affiliation_string: Department of Educational Technology, Ibn Khaldun University, Bogor, Indonesia.
raw_author_name: Arita Marini
raw_affiliation_string: Department of Elementary School Teacher Education, Universitas Negeri Jakarta, Indonesia

Abstract: This research explores lecturers' perceptions of digital libraries as a learning resource for the industrial era 4.0 that can be used anytime and anywhere to solve the low interest in developing lecturers because of the difficulty of accessing references to conventional libraries, which require lecturers to visit the library building. Data was collected through semi-structured interviews with 15 lecturers, and data analysis was performed using thematic analysis. The analysis results found four main themes: benefits, challenges, recommendations, and positive and negative impacts of digital libraries. The research concluded that the existence of a digital library can make it easy for lecturers to browse the references needed quickly, easily, and flexibly just by entering the library's website page. This research contributes to solving the problem of lecturers in Indonesia over the limited learning resources in conventional libraries that are difficult to access online and require particular visiting time because they have to come directly during library operating hours. Therefore, support from the government, university leaders, and the wider community is needed to develop a digital library system to facilitate lecturers in Indonesia having adequate learning resources for the industrial era 4.0.
Analysing the Nexus between the Financial Sector and Economic Growth in Nigeria: A Comparative Investigation using, BVAR, Linear Regression (OLS), and PPML Models

Authors:
raw_author_name: Sabinus Nnamdi Nwanneako
raw_affiliation_string: Department of Mathematics, Rivers State University, Port Harcourt, Rivers State, Nigeria.

Abstract: Aims: This study aims to analyze the complex relationship between the financial sector and economic growth in Nigeria. The study aims to provide comprehensive insights into this nexus by employing a comparative investigation using three distinct models: Linear Regression, Poisson Pseudo Maximum Likelihood (PPML), and Bayesian Vector Autoregression (BVAR).&#x0D; Methodology: The study then applied three different models, with a specific focus on the BVAR(2) model, supported by various diagnostic tests and stability assessments. The inclusion of Linear regression analysis and Poisson Pseudo Maximum Likelihood Estimator (PPML) enhances the depth of the study, providing nuanced insights into the impact of specific financial sector variables on economic growth.&#x0D; Results: The BVAR (2) model emerges as the optimal choice, demonstrating its reliability in capturing dynamic interactions and offering a powerful tool for policymakers. Specific results, such as the significant negative impact of D(CPS) in the regression analysis and the high R-squared in PPML, provide actionable insights into areas requiring policy interventions and underscore the substantial contribution of the financial sector to economic growth.&#x0D; Conclusion: The comparative assessment of model performances, favoring the BVAR model, guides future research and policy considerations, providing a reliable framework for further investigations. The study's insights are positioned as valuable for policymakers seeking to enhance economic growth through strategic interventions in the financial sector. Overall, the abstract succinctly encapsulates the aims, methodology, results, and concluding implications of the study on the nexus between the financial sector and economic growth in Nigeria.
Study of Academic Achievement of Mathematics Subject in the Context of Study Habits of Students

Authors:
raw_author_name: None Dr. Neeraj Yadav
raw_affiliation_string: Assistant Professor, Department of B.Ed., Shri Lal Bahadur Shastri Degree College, Gonda, (U.P)

Abstract: Students have different study habits from one another and from location to location. It is a crucial component of education since students' study practices have a big effect on their achievement in academics. Parents, educators, administrators, and members of the public are increasingly concerned about the poor level of mathematical comprehension in our country. Researchers and educators have worked very hard to determine the causes of the low performance in the subject. Despite all of these initiatives, the issues persist. Students' study habits are a powerful indicator of both their academic performance in mathematics and the quality of their education. It is unrealistic to expect students to acquire all there is to know about a subject from their teachers in the classroom; students' study habits are a combination of what they learn in and outside of the classroom. Sorenson (1991) listed several beneficial study habits, one of which is that the main goal of study should be understanding. One must focus on this for an extended period of time rather than rushing through. Effective study techniques, according to Crow &amp; Crow (1992), include setting up a specific workspace, creating a timetable, and taking concise, well-organized notes.
Analyze and Forecast the Cyber Attack Detection

Authors:
raw_author_name: Prof. Prakash Kshirsagar

Abstract: Abstract: The relentless evolution of digital technologies has ushered in an era of unprecedented connectivity and convenience, but it has also exposed our digital infrastructure to a growing spectrum of cyber threats. In this context, the importance of robust cyber attack detection mechanisms cannot be overstated.The increasing complexity and sophistication of cyber threats necessitate equally advanced detection techniques. Cyber attack detection plays a pivotal role in safeguarding digital assets and maintaining the integrity of critical systems. This abstract delves into the foundational principles and contemporary strategies employed in this domain. Detecting cyber attacks presents a multifaceted challenge due to the ever-evolving tactics employed by malicious actors.
PEMBENTUKAN KARAKTER PEDULI SOSIAL MELALUI PEMBELAJARAN IPS DI SDI ENDE 14

Authors:
raw_author_name: Manggu Ngguna Raji
raw_affiliation_string: Program Studi Pendidikan Guru Sekolah Dasar, Universitas Flores, Indonesia
raw_author_name: Fransiskus Korosando
raw_affiliation_string: Program Studi Pendidikan Guru Sekolah Dasar, Universitas Flores, Indonesia

Abstract: This research aims to describe the formation of social caring character through learning Social Sciences at SDI Ende 14, Mautapaga Village, East Ende District, Ende Flores Regency, East Nusa Tenggara. This study uses a qualitative method. Data collection techniques in this research were interviews, observation and documentation as well as questionnaires. The data analysis techniques in this research are data reduction, data presentation and drawing conclusions. The results of this research show that the formation of a socially caring character through learning Social Sciences is able to change the character of students for the better. Apart from learning in class, social care character formation is carried out through direct learning practices. After getting the theory from the teacher and being able to practice it in direct learning, students apply the character of social care, it becomes a habit and becomes a culture at SDI Ende 14, Mautapaga Village, East Ende District, Ende Flores Regency, East Nusa Tenggara.