Reviewerly/exHarmony huggingface dataset:
A Systematic Survey on E-VOTING SYSTEM USING BLOCKCHAIN TECHNOLOGY

Abstract–An electronic voting system based on blockchain technology, addressing the challenges of traditional paper ballots and digital voting methods. The system aims to provide security, integrity, transparency, and privacy for voters. It evaluates various blockchain frameworks and proposes a novel e-voting system that can be particularly useful for small-scale elections within corporate environments. The implementation relies on Ethereum’s smart contracts, with development and testing facilitated by the Truffle framework and Ganache as the Ethereum client for testing. It also highlights the historical challenges in developing secure electronic voting systems that balance fairness and privacy with transparency and flexibility. The paper explores the application of blockchain technology in addressing these issues, emphasizing the potential of distributed ledger technology for hosting nationwide elections securely and cost-effectively. The paper also underscores the significance of blockchain technology in ensuring anonymity, privacy, verifiability, and fairness in the voting process. Keywords- Blockchain, Decentralized, Ethereum, E-Voting, Smart Contracts.
A Survey Paper on Automation of Attendance System through Facial Recognition

Abstract—In recent times almost, everything around us is being automated in some way just to make life easier for us. With the use of machine learning and deep learning we can allow non- living objects to program themselves to self-learn from the available data and perfect itself in the process. The process of recording attendance in educational institutions and workplaces has traditionally relied on manual methods, which are time- consuming, error-prone, and lack the level of security and accuracy desired in today’s digital age. This research presents a technological solution to these challenges. This technology offers a range of compelling advantages. It eliminates the need for manual attendance tracking, reducing the potential for errors and administrative workload. Moreover, it enhances security by ensuring that only authorized individuals can mark their attendance. Real-time data is available for monitoring and generating attendance reports, offering valuable insights into punctuality and attendance patterns. This research provides a comprehensive overview of the system’s architecture, algorithms, and the underlying technology, along with a discus- sion of the ethical and privacy considerations associated with face recognition. By implementing this innovative technology, educa- tional institutions and organizations can streamline attendance management, increase operational efficiency, and contribute to a more secure and productive environment. Keywords: Face recognition, LBPH, Haar-cascade, face detection, Viola n Jones, openCV, Adaboost
Comparative Analysis of Sentiment in Original and Summarized Tweets: Leveraging Transformer Models for Enhanced NLP Insights

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

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

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
Classifying Opinions and Sentiments on Social Networking Sites using Machine Learning Classifiers

Abstract: People now publish evaluations on social media for any product, movie, or location they visit as a result of the Web's rapid development. Customers and product owners can both benefit from the reviews posted on social media in order to assess their offerings. Compared to unstructured data, structured data is simpler to analyze. The reviews are mostly available in an unstructured format. Aspect-Based Sentiment Analysis extracts from the reviews the features of a product and then calculates sentiment for each feature. Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone expressed in a piece of text, such as a sentence, paragraph, or document. Machine leaning classifiers are used to classify sentiments. Machine learning classifiers cannot process raw text so raw text needs to be converted into vectorized form. Feature extraction techniques are used to convert raw text to numerical form also called vectorized data. In present research, four feature extraction techniques with five different machine learning classifiers namely, SVM, Logistic Regression, Naïve Bayes, Random Forest, and KNN are applied to classify sentiments associated with tweets. Two online twitter data sets containing tweets about product reviews and tweets about people's thoughts on public policy are selected for experimentation. In the experiments done, it has been found that the SVM classifier using TFIDF and HFE shows better performance as compared to other classifiers. Using the feature sets, 97% accuracy and 98% F1- score is achieved in the aspect category prediction task.
Studi Literatur Mengenai Prediksi Kepuasan GrabFood Menggunakan Machine Learning

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

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.
WorkTune: A Music-Assisted Writing Efficiency Evaluation and Promotion Platform using Artificial Intelligence and Natural Language Processing

In a world where music accompanies various tasks, our paper addresses the challenge of understanding the impact of background music on work efficiency. The background problem centers on the lack of precision in existing studies, overlooking individual preferences and work types. Our proposed solution is a Python-based application that evaluates an individual's work efficiency while listening to different music genres [1]. The user-friendly interface incorporates features like music category selection, login options, and real-time statistics tracking [2][3]. Challenges, such as diverse user interactions and limited data, were addressed through a feedback channel for continuous improvement. The application underwent experiments, including regression model evaluations for essay grading and SVM parameter tuning [4]. Results indicated superior performance, emphasizing the relevance of ensemble learning and optimal parameter selection. This application provides a nuanced understanding of how background music influences work efficiency, offering a personalized approach that people can leverage for enhanced productivity and satisfaction in various work scenarios.
An Optimized Writing Efficiency Tool with Integrated Gamification and Work Interval Timer for Enhanced Essay Productivity and AI Advice

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

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.
An Efficient Domain-Independent Approach for Supervised Keyphrase Extraction and Ranking

We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly lightweight ensemble model. Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several state-of-the-art baseline models, including all deep learning-based unsupervised models compared with, and is competitive with some supervised deep learning-based models too. Despite the supervised nature of our solution, the fact that does not rely on any corpus of “golden” keywords or any external knowledge corpus means that our solution bears the advantages of unsupervised solutions to a fair extent.
Impact of 5G on Internet of Things and its Challenges

In this era of rapid technological growth, the Internet of Things (IOT) refers to a system of interrelated, internet-connected objects that are able to collect and transfer data over a wireless network without human intervention. We see the IOT as billions of smart, connected things (a sort of ―universal global neural network in the cloud) that will encompass every aspect of our lives, and its foundation is the intelligence that embedded processing provides. The IOT is comprised of smart machines interacting and communicating with other machines, objects, environments and infrastructures. As a result, huge volumes of data are being generated, and that data is being processed into useful actions that can ―command and control things to make our lives much easier and safer—and to reduce our impact on the environment. The creativity of this new era is boundless, with amazing potential to improve our lives. The following thesis is an extensive reference to the possibilities, utility, applications and the evolution of the Internet of Things. The rollout of commercial 5G cellular networks has commenced, marking a significant milestone in the adoption of 5G and the Internet of Things (IoT). This adoption is propelled by various factors, such as rising consumer and enterprise demands, coupled with the availability of cost-effective devices. Notably, substantial investments by operators in 5G technology, spectrum allocation, and infrastructure development, alongside the establishment of global standards, are key drivers fuelling growth and generating heightened market interest in the IoT. The substantial investments made by operators in 5G technology, spectrum allocation, and infrastructure, coupled with the adoption of global standards, play a pivotal role in stimulating growth and generating heightened market interest in the Internet of Things (IoT). The ongoing evolution of 5G mobile cellular networks, derived from existing 4G frameworks, underscores their adaptability to diverse use cases, both present and future-oriented. With a forward- looking approach, 5G infrastructure is poised to address current demands like smart energy applications while proactively accommodating forthcoming innovations such as autonomous vehicles. In navigating this technological transition, mobile operators face the challenge of ensuring that their networks remain adept at supporting a spectrum of present and future use cases. Strategic investment management is imperative for operators to seamlessly transition their networks to 5G while upholding the continuity of service for their customers Key Words: Scalability, safety, security, rapid growth, interoperability, devices, interconnections, 5G with Internet of things, healthcare, smart city
Prediksi Diabetes Melitus Tipe-2 Menggunakan Sequential Forward Selection (SFS) Dengan Algoritma Support Vector Machine (SVM)

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

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.
Ability to organize experiential activities of teachers in primary schools

This study examines the capacity of primary school teachers to design, organize, and evaluate experiential activities, utilizing a robust measurement framework grounded in the 2018 General Education Program. The evaluation encompasses 540 teachers, drawing on diverse sources, including questionnaires, regulatory documents, and input from educational stakeholders. The findings highlight commendable strengths in planning and organizing experiential activities, with 324 teachers demonstrating excellent or good capacity. However, critical gaps are evident, particularly in contextual analysis, testing tool design, and information processing, reflecting a need for targeted professional development. The measurement framework, calculated with a distance value of 0.8, employs a scale categorization that offers nuanced insights into the varying impact of experiential activities. The study underscores the significance of targeted professional development initiatives tailored to address identified gaps. Curriculum adaptations are recommended to leverage successful strategies, and informed decision-making is facilitated by evidence-based policies. The findings advocate for a culture of continuous improvement in educational practices. While the study provides valuable insights, limitations include contextual specificity, potential subjectivity in self-reported data, and the exclusion of student perspectives. A quantitative emphasis, reliance on a single methodology, and a sample size of 540 teachers may impact generalizability. This research informs educational stakeholders, policymakers, and practitioners about the current state of experiential education in primary schools. The study, within its limitations, contributes actionable insights to enhance the effectiveness of teacher training and curriculum design, fostering an environment conducive to experiential learning practices.