Reviewerly/exHarmony huggingface dataset:
Politeness Strategy Analysis on the Tonight Show Starring Jimmy Fallon

The purpose of this study is to identify the politeness strategies used by Taylor Swift and Paul Rudd in the Tonight Show with Jimmy Fallon YouTube videos "Taylor Swift Talk Record-Breaking Midnight Album, Music Video Cameos and Easter Eggs" and "Paul Rudd Compares His Men's Health Shoot with Jonathan Majors' (Extended)" when they decide to use particular politeness strategies in communication. The research employed a qualitative descriptive method using an observational data collecting technique is applied to analyze the speakers' manner politeness strategies affect the communication between the speaker and hearer. Data collected from observation were analyzed using discourse analysis interpretation and were classified regarding the Brown and Levinson (1987) theory. This research found that the speakers both of Taylor and Paul prevalence the positive politeness strategy when they communicating in Jimmy Fallon’s show. the positive politeness strategy that appeared in Taylor Swift that she is usually showing exaggerate (interest approval, sympathy with Hearer) while Paul Rudd deals with his character who really like making a joke almost in the whole conversation. Besides, the differences also followed by the second strategies that revealed by Taylor and Paul. Taylor Swift commonly chosen negative politeness strategy and Paul Rudd deals with off-record politeness strategy.
STUDY OF SCREEN TIME AND OBESITY AMONG SCHOOL GOING ADOLESCENTS OF URBAN SLUM, JAIPUR

Background: Excessive screen time for more than 2 hours daily among adolescents has been found to be associated with increased psychosocial problems, hypertension, elevated cholesterol and obesity among adolescents. Objective: To assess screen time of school going adolescents and to nd out factors associated with it. Material and Methods: Observational cross-sectional study was conducted among adolescents of randomly selected co-ed school in UHTC eld practice area, S.M.S. Medical College, Jaipur from January 2023 to March 2023. Students of class 9th to 12th were interviewed and their BMI was calculated using standard methods. Results: Out of 80 adolescents students 56.25% were boys and 63.75% were in age group &gt;16 years. Average screen time was 77.9+47.5 hours per week. 10% of adolescents were found over weight (BMI &gt;25). Conclusion: Present study found that screen time was more than recommended in older children (&gt;16years) and boys were signicant users of electronic devices. Overweight was present in 10% of adolescents.
Rancang Bangun Aplikasi Presensi Online Menggunakan QR Code Scanner dan Lokasi Berbasis Android

AMIK HASS Bandung is a private university engaged in D3 education with an Informatics Management study program. AMIK HASS Bandung has many activities in each of his learning activities, one of which is the attendance process. The attendance data collection process is carried out by filling out and collecting documents that have been filled out by students or employees so that the data recording process is still a bit long, it is necessary to have an application that helps the data management process more effectively and report results quickly, precisely and accurately. In the preparation of this final report, there are several methods of analyzing, namely Class Diagrams, and prototypes. In addition, the author also uses data collection techniques in the form of observations related to this research and the software used is PHP, Java, Android Studio and MySQL
RANCANGAN SISTEM INFORMASI PEMBELIAN BARANG DENGAN METODOLOGI BERORIENTASI OBYEK

In companies / agencies need an information system for purchasing goods so that it can be done computerized and not done using Microsoft Office. Purchasing goods is an overall process used to hold something necessary in meeting the needs of goods in the company. The need for goods is very important in supporting the work process in a company / agency, the unavailability of goods can hamper performance. Supervision of requests is generally less than optimal, because many procurement request files are still stored in the File and sometimes some are torn and lost, so a system is needed that handles the request process. The process of making requests for goods in the procurement section has difficulty updating the availability of goods so that it hampers the process of demand for goods, so a module is made to be able to monitor the process of demand for goods and activities related to the purchase of goods. So that the procurement department can speed up the process of disbursing data, minimizing errors and data loss. The system that is created later uses a Visual .Net programming language with a MySQL database, all processes will be stored into the MySQL database.
ANALISIS BIBLIOMETRIK PERKEMBANGAN PENELITIAN BIDANG ILMU GEOGRAFI

Research data and publications that have been carried out can be essential data in bibliometric analysis to determine the development of an institution's publication profile. This study aims to analyze the research publication by geography lecturers at Gajah Mada University, indexed by Scopus in 1991Scopus in -2021. This analysis includes the distribution of publications, types, sources of publication documents, the most prolific authors, collaborating affiliations, and trending publication themes.The research method used was a quantitative descriptive method by conducting a bibliometric analysis of the publication of the lecturers of the Faculty of Geography, Gadjah Mada University, indexed by Scopus.The findings showed that (a) there were 801 total publications, with an increase in the number of publications since 2014 (8 documents) and peaking in 2020 ( 218documents); (b) the most published documents were conference papers (58.67%);(c) Most source documents from the IOP Conference Series Earth and Environmental Science; (d) Most lecturers collaborated with the Laboratoire de Géographie Physique; (e) The most productive lecturer was Marfai, M.A.; and (f) Research trends themed Worldview-2, Mapping, Remote Sensing, GIS, and Tsunami.It is necessary to increase the number of publications in academic journal, due to the publications are dominated in conference papers at present.
Detection of Sarcasm Using Bi-Directional RNN Based Deep Learning Model in Sentiment Analysis

Detecting sarcasm in text can be challenging, but it's an opportunity to exercise the ability to consider various perspectives and aspects. Considering multiple viewpoints while analysing sentences can lead to comprehensive results and constructive discussions. Sentiment analysis algorithms are constantly improving and becoming more accurate in identifying sarcasm, even in implicit expressions where the sarcasm is not explicitly stated. It's exciting to have the opportunity to detect sarcasm conveyed through subtle cues, as it adds another layer of complexity to the task. The field of sarcasm detection has made great strides with the use of supervised classification models that can accurately distinguish between sarcastic and non-sarcastic sentences through labelled data. This approach shows promise for identifying explicitly incongruous statements, and there is an increasing need for techniques that can successfully detect sarcasm in sentences with implicit sentiment incongruity. This study employs a deep learning model to tackle the challenge of distinguishing implicit sarcasm. The model being used is a recurrent neural network (RNN), which has the ability to retain numerical representations of previously processed information. The proposed model is designed to effectively identify sarcasm by capturing context-based incongruity within the presented text, which is a crucial factor. The proposed model, Proposed LSTM (P-LSTM), effectively captures the dependencies among words in both suffixes and prefixes by scanning the given sentences bi-directionally. The classification is organised into two levels: emotional and semantic. The suggested approach offers a way to accurately evaluate and categories tweets based on their sarcastic state. Excitingly, the proposed method is being evaluated through experiments on two automatically annotated datasets and two manually annotated datasets to determine its effectiveness. The suggested model is compared to numerous state-of-the-art methods, highlighting its efficacy. The findings demonstrate that the suggested model is highly effective for sarcasm detection compared to other methods currently in use. The suggested model has great potential in recognising implicit sarcasm by utilising deep learning and context-based incongruity analysis, which could lead to significant improvements over existing methods.
Storing System in Improving Smoothness Goods Operations at Luwansa Beach Resort

The storage system is very important for every hotel because it can help improve the smooth operation of goods, so this study aims to analyze the string system implemented at the Luwansa Beach Resort Hotel in improving the smooth operation of the hotel. This type of research is qualitative research. The data collection techniques used observation and direct interviews with sources at Luwansa Beach Resort. The results of this study indicate that the storage and disposal of goods at the Luwansa Beach Resort Hotel has implemented a storage system and procedures using the first in first out (FIFO) system and the first expired first out (FEFO) system.
Long-short term memory prediction of user’s locomotion in Virtual Reality

Abstract Virtual reality has some well-known limitations when the user walks naturally. A user experiencing virtual reality may collide with the limits of the physical workspace. To solve this, there exist techniques, such as the redirected walking methods, that try to trick the human brain to avoid these limits. However, these methods require good predictions of where users want to go in the future. On the other hand, Deep learning is helping in so many areas to make accurate predictions. Specifically, Long short-term memory obtained promising results in this virtual reality prediction task giving us clues to continue researching in this line. This manuscript presents an experiment carried out with 44 participants collecting positional data. These data have been used to test different existing prediction algorithms. In addition, a way to improve the previous results obtained with deep learning is presented. These results are achieved with the use of rotation quaternions and various positional data in three dimensions. The authors strongly believe that there is still much room for improvement in this research area.
Development of Lift the Flap Book Learning Media on the Theme of Care About Living Things in Class IV SD

This study aims to develop learning media lift the flap book on the theme of caring for living things and to determine the feasibility of the media. This research is a type of development research (Research and Development) using the ADDIE development model. This research was carried out in three stages, namely 1) the analysis stage consisted of teacher analysis, student analysis; 2) the design stage, namely the design of the lift the flap book learning media using the Microsoft Word application and online applications that support it; 3) the development stage includes the validation stage. The subjects in this study were 2 media expert validators and 2 material expert validators. The instrument used to collect data is a questionnaire. The data analysis technique used in this research is descriptive qualitative and descriptive quantitative. Based on the feasibility of the media from the input of material experts, namely lecturers and teachers, media experts, namely lecturers. Reached 74%, material experts (teachers) reached 81%. If the proportion obtained reaches 60% to 80% then lift the flap book media is properly included. From the calculation above, it is obtained that the proportion is 74% and 81%, so the lift the flap book media is feasible. Media experts reach 90% and 85%. If the proportion obtained reaches 80% to 100% then the lift the flap book media is very feasible. From the calculation above, it is obtained that the proportion is 90% and 85%, so the lift the flap book is categorized as very good/very feasible. From the data validation results, the development of lift the flap book media on the theme of caring for living things developed by researchers was declared very feasible to be used as learning material in the learning process.
Augmented Reality Animation Image Information Extraction and Modeling Based on Generative Adversarial Network

Computer-aided design (CAD) has been applied more and more in the field of art design, which has become an indispensable tool for art designers, and art design has also ushered in a new development opportunity.Artificial intelligence (AI) is widely used in art design, which can better interpret the designer's interpretation of works and their design concepts artistically and present them to people.In this article, the characteristics of color images of artworks are analyzed, and a deep learning (DL) model based on improved particle swarm optimization (PSO) algorithm is used to track and extract the contours of artworks, so as to realize the recognition of color characteristics, and the CAD 3D reconstruction of artworks is completed according to the recognition results.The comprehensive results show that this method not only improves the efficiency of artistic image processing compared with the traditional DL method, but also has obvious advantages in image recognition accuracy.Therefore, the improved PSO algorithm is used to optimize the CAD modeling stage of artworks, which can locate the edge contour of artworks relatively accurately on the premise of ensuring the clarity of artworks images, thus improving the efficiency of artistic design and expanding the artistic design ideas.
POETIC TRANSLATION AS A MULTIVARIATE PHENOMENON: THE PROBLEM OF PRESERVATION AND OPTIMIZATION OF THE AUTHOR’S INDIVIDUAL STYLE IN TRANSLATION

The issue of poetic translation, its peculiarities, difficulties and importance still occupies one of the leading positions in the translation sphere.It is especially essential if the question is regarded with an individual style of an author and the problem of transferring, preservation and optimization of this style in translation.The question of an individual style of an author and the specifics of its singling out is also within the interests of modern linguistic sciences connected with the translation sphere.An individual style of an author is understood as a set of characteristics peculiar to an author in all his works created during his whole creative path or done within a definite period of his creativity.If to take into consideration the problem of preserving an individual style of an author in translation, it is necessary to mention several important questions.Firstly, it is the way an individual style of an author can be defined, that is, through literary and linguistic approaches.Secondly, why it is important to preserve an individual style of an author in translationan individual style reflects the vision and the world perception of an author, and creates the general notion about the author himself.Thirdly, why the process of preserving an individual style of an author is considered to be a complicated act.It is because poets often act as translators dealing with the translation of poems.In this aspect one may say about influence of poettranslator's style on the original author's style.And, finally, the importance of optimizing an author's style in translation lies in the necessity of creating the same feelings of a target language reader as a source language reader has.Thus, the process of preserving and optimization of an author's style in translation is of importance in translation and should always be taken into consideration.
FIGURATIVE CONCEPTUAL AND CREATIVE METAPHORS AND THE FEATURES OF ITS RECREATION IN EMILY DICKINSON'S POETRY

Шпак Л. Р
(Towards a) Statistical Probabilistic Lazy Lambda Calculus

We study the desiderata on a model for statistical probabilistic programming languages. We argue that they can be met by a combination of traditional tools, namely open bisimulation and probabilistic simulation.
Contextual Emotion Recognition Using Transformer-Based Models

&lt;p&gt;In order to increase the precision of emotion identification in text, this research suggests a context-aware emotion recognition system employing transformer models, especially BERT. The model is able to comprehend complex emotions and context-dependent expressions since it was trained on a broad, emotion-labeled dataset. On a benchmark dataset, its efficacy is assessed in comparison to conventional techniques and standard transformer models. The system is proficient at gathering contextual information, and the findings demonstrate a considerable improvement in emotion recognition accuracy. This study improves textual emotion identification, opening the door to applications like chatbots that can recognize emotions and systems for tracking mental health. It also identifies potential areas for further study in developing transformer models for context-sensitive NLP applications.&lt;/p&gt;
Multi-Position Human Activity Recognition using a Multi-Modal Deep Convolutional Neural Network

Human Activity Recognition (HAR) is a challenging task due to the complexity of human motions and the variability of datasets. The wide adoption of wearable devices and the incorporation of high-grade motion sensors and biosensors in these devices increased the number of available data that could be employed in sensor-based HAR. In this paper, we propose a multi-modal deep convolutional neural network capable of recognizing different activities using accelerometer data from several body positions. We compare the performance of our proposed system with existing DCNN model architectures. Our experiments on two public datasets for HAR demonstrated that our approach surpassed the performance of both single-position and simple multi-position DCNN models. This study provides valuable insights for the development of efficient edge-AI systems for activity recognition on resource-constrained embedded devices.
Impact of Artificial Intelligence on Digital Marketing

Keywords: Artificial Intelligence (AI), Machine learning (ML), Digital Marketing (DM) Research question: How does AI affect digital marketing and help digital marketers to improve their performance? Purpose: The aim of this study is to determine the Impact of AI in digital marketing by analyzing and determining how digital marketers utilize AI to improve performance. Method: This is a quantitative study by conducted survey questions and analyzed using chart. Conclusion: AI has the potential to revolutionize digital marketing by automating various processes and enabling marketers to better understand and meet customer needs. As digital marketers strive to enhance their strategies and engage with customers effectively, they recognize the value of AI in achieving their goals.
Efficiently Encryption Decryption schema for Secure File Storage System in Cloud Computing

Cloud computing finds applications in different industries, providing various services and data storage capabilities. While data stored in the cloud can be retrieved based on user requests, data security remains a primary concern. To address these security concerns, a proposed system employs the combined strength of the Triple Data Encryption Standard (AES) and Blowfish algorithms. The lack of robust and efficient secure file storage systems in cloud computing poses significant risks to data confidentiality and integrity.Existing solutions often lack strong encryption mechanisms and reliable server-side encryption and decryption capabilities. This research aims to develop a secure file storage system that leverages server encryption and decryption techniques to ensure data confidentiality, integrity, and access control in cloud environments. The objective is to design a system that effectively protects sensitive files during storage, transmission, and processing, while maintaining efficient data access and retrieval for authorized users. Key Words: Server-Side Encryption, Secure Key Storage Decryption, Encryption, Secure File Storage, Blowfish
Moving horizon estimation of xenon in pressurized water nuclear reactors using variable-step integration

A specific moving horizon estimation scheme is proposed for reconstructing the axial xenon concentrations in pressurized water nuclear reactors (PWR). The model considered is continuous time, with a parcimonious spatial discretization. The estimation scheme involves continuous time integration while using low frequency sampling of measurements. The results support a sensitivity analysis. The quality of the estimation is shown to be good, even though the model is particularly stiff, and the implementation altogether light.
Malicious URL Detector using Machine Learning

In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user’s system and causing losses of billions of dollars each year. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. In this research paper we have implemented the application that it can check the URL given by the user is benign, defacement, malware, phishing. After checking then it will display the type of URL in user interface. The increasing sophistication of cyber threats, particularly in the form of malicious URLs, poses a significant challenge to internet security. Traditional signature-based approaches and heuristic methods have proven insufficient in detecting rapidly evolving malicious URLs.This research paper presents a comprehensive study on the development and evaluation of a malicious URL detector based on machine learning known and novel malicious URLs. Keyword: Phishing, URL, machine learning, cyber security, random forest, malicious.
Sentimental Analysis Of COVID-19 Vaccination Tweets Using Machine Learning Techniques

Twitter Sentiment Analysis applies sentiment analysis to data in tweets on social media platforms to identify user sentiment. The scope of research in this field has expanded steadily in recent decades. The reason is that the format of tweets is complex and difficult to process. The extremely small Tweet format introduces a whole new set of problems: B. Use of Slang and Acronyms. The global community remains affected by the ongoing COVID-19 pandemic, with its profound implications for the health and overall welfare of individuals worldwide. Our worldview has changed as a result of the pandemic. A vaccination campaign should be carried out among the population to stop the outbreak of the pandemic. However, people are unsure about vaccination, which is cause for concern. In this study, machine learning techniques were employed to assess the sentiment of public Twitter tweets concerning COVID- 19 vaccination. The researchers utilized two distinct ML methods, namely Support Vector Machine (SVM) and Logistic Regression (LR), to analyze the Twitter data and classify the tweets as either positive or negative. Keywords:Classifier, Twitter, Sentiment Analysis.