Reviewerly/exHarmony huggingface dataset:
Implementation of School Literacy Movement of The Class XI SMK Kesehatan Haji Sumatera Utara

&#x0D; &#x0D; &#x0D; &#x0D; The purpose of this research was to know the students’ implementation of school literacy program. The design of this research was descriptive quantitative research by using Survey method. The instrument of this research was questionnaire to know the implementation of students toward the school literacy program. The population of this research was all of the eleventh grade students of SMK Kesehatan Haji Sumut which consisted of 3 majors and 59 students. The sample was all of the population because this research was using total sampling technique The instrument validity test was carried out by using expert validity to three experts as instrument validators and to analyze the data was done by identifying the responses, counting the percentages of each indicator and taking the conclusion. The results of the research showed that the average score of the habituation stage of school literacy program was 38,07% which was good perception. The average score of the development stage of school literacy program was 38,22% which was good perception. The last, the average score of the learning stage of school literacy program was 38,66% which was good perception. It can be concluded, the average mean of students’ perception toward the implementation of school literacy program was 38,30% which was in good category in the rating interpretation 60%-39,99%.&#x0D; &#x0D; &#x0D; &#x0D;
Pulling and Pushing

In this chapter, we’ll make use of raycasting to allow the player to point their camera at a GameObject with a Rigidbody and hold left-click to pull the object toward them or right-click to push it away from them. Rather than pulling and pushing by moving the Transform directly, we’ll apply force to the Rigidbody so that the physics system handles the motion for us. This will demonstrate how to manipulate Rigidbodies by applying forces to them.
Force Fields and Jump Pads

In this chapter, we’ll be implementing a configurable ForceField script that pairs with a trigger collider to apply force to Rigidbodies and/or the player. Based on a variable set in the Inspector determining the type of force to apply, we’ll either apply it constantly while the trigger is touched or once when the trigger is first entered. In other words, based on the force mode you choose, you could make an object either get pushed as if by a large fan, or get shoved up like a “jump pad” thrusting them all at once.
A novel generalized adversarial image method using descriptive features

Abstract— Currently, machine learning not only solves simple problems such as object classification but also machine learning is widely applied in the field of computer vision such as identification systems, object detection, and modules in the authentication system, intelligent processing algorithms such as automatic driving, chatbot, etc. Deep learning models on GAN networks can automatically generate image data such as objects, animals, human faces, or the like by learning the word features of images in datasets such as MS-COCO, ImageNET, CUB, etc. Using this technique, attackers can fake images in some cases with malicious intent. In this paper, the authors propose to build a Generative Adversarial Network to create images that fool the target model YOLOv7, INCEPTIONv3. Experimental results on the CUB dataset show our proposed model's ability to generate adversarial examples is highly effective with an average image generation time equal to 0.16 seconds/an image. The successful rate of fooling the model reached over 85%, average recognition rate reached over 45% for the YOLOv7 model. When experimenting on the INCEPTIONv3 model, the successful rate of fooling the model reached over 95%, average recognition rate reached over 50%. The image fidelity evaluated by the PSNR index reached an average of greater than 29.
Efektivitas metode Picture Exchange Communication System (PECS) untuk meningkatkan perbendaharaan kata pada anak dengan Autism Spectrum Disorder

This study aims to investigate the effectiveness of the Picture Exchange Communication System (PECS) method in increasing vocabulary in children with autism spectrum disorder level 2. The hypothesis put forward in the study is that the PECS method is effective in increasing vocabulary in children with autism spectrum disorder level 2. The research method was a quasi-experiment with a one-group pretest posttest design. Data were collected using, observation, and interviews. The test tools for the assessment included the Coloured Progressive Matrices (CPM) test and the Binet test, the Children Autism Rating Scale (CARS), and the Vineland Social Maturity Scale (VSMS) test. Data were analyzed using the Paired Sample t-test and the results showed a significance level of 0.02 (smaller than 0.05), Based on the results of the study, it can be concluded that the PECS method is effective in increasing vocabulary in children with autism spectrum disorder level 2. Penelitian ini bertujuan untuk mengetahui efektivitas metode Picture Exchange Communication System (PECS) dalam meningkatkan kosa kata pada anak dengan gangguan spektrum autisme level 2. Hipotesis yang diajukan dalam penelitian adalah metode PECS efektif dalam meningkatkan kosa kata pada anak autis. gangguan spektrum tingkat 2. Metode penelitian yang digunakan adalah quasi eksperimen dengan desain one-group pretest posttest. Pengumpulan data dilakukan dengan menggunakan observasi dan wawancara. Alat tes yang digunakan dalam penilaian antara lain tes Colored Progressive Matrices (CPM) dan tes Binet, Children Autism Rating Scale (CARS), dan tes Vineland Social Maturity Scale (VSMS). Data dianalisis menggunakan uji Paired Sample t-test dan hasilnya menunjukkan tingkat signifikansi sebesar 0,02 (lebih kecil dari 0,05), Berdasarkan hasil penelitian dapat disimpulkan bahwa metode PECS efektif dalam meningkatkan kosakata pada anak autis. gangguan spektrum tingkat 2.
Validity of Large Language Models for Sentiment Analysis: Evidence of performance comparable to human coders

Assessing the sentiment of content is a major focus of communication science. Previous research has compared the performance of ‘gold-standard’ methods – exemplified by trained human coders – with approaches such as crowd-sourcing, data dictionaries, and machine learning. Aggregated crowd-sourced assessments and trained human coders perform best, but trained coders may not be scalable and crowd-sourcing may raise financial, ethical, and practical issues. Large Language Models (LLMs) – artificial intelligence systems trained on vast datasets to process and generate human-like text – may combine the in-depth understanding of human coders with the speed and scalability of automated methods. We used the gold-standard sentiment coding for a corpus of Dutch economic news headlines from previous research. We found that two commercially available LLMs, Anthropic’s Claude 2 and OpenAI’s ChatGPT-4, could perform at levels approaching or matching single trained coders and crowdsourced ratings, and reliably exceeded previously reported machine learning and automated approaches. Using variation in prompts (‘prompt engineering’), we show that prompt details can affect performance, but even with minimal instruction the performance of Claude 2 approached human-level. LLMs are promising tools for scalable and high-quality sentiment analysis. Future research can consider their applications on long-form text and images.
Smooth Subsum Search A Heuristic for Practical Integer Factorization

The two currently fastest general-purpose integer factorization algorithms are the Quadratic Sieve and the Number Field Sieve. Both techniques are used to find so-called smooth values of certain polynomials, i.e., values that factor completely over a set of small primes (the factor base). As the names of the methods suggest, a sieving procedure is used for the task of quickly identifying smooth values among the candidates in a certain range. While the Number Field Sieve is asymptotically faster, the Quadratic Sieve is still considered the most efficient factorization technique for numbers up to around 100 digits. In this paper, we challenge the Quadratic Sieve by presenting a novel approach based on representing smoothness candidates as sums that are always divisible by several of the primes in the factor base. The resulting values are generally smaller than those considered in the Quadratic Sieve, increasing the likelihood of them being smooth. Using the fastest implementations of the Self-initializing Quadratic Sieve in Python as benchmarks, a Python implementation of our approach runs consistently 5 to 7 times faster for numbers with 45–100 digits, and around 10 times faster for numbers with 30–40 digits. We discuss several avenues for further improvements and applications of the technique.
Aesthetics and Artificial Intelligence: Impact and Criticism of Art

The background of this research is the technological disruption in the digital era like today in the field of art. This research takes a point of view from the branch of philosophy, namely aesthetics, to examine the impact and criticism that arises. More specifically, explaining how this aesthetic meets artificial intelligence integrated with computers, resulting in art, artists and conventional works of art starting to feel the impact. The aim of this research is to determine the impact and criticism from philosophers or scientists regarding the use of artificial intelligence in the process of creating works of art. This research uses qualitative methods and literature studies from books and journals related to the research theme. The findings from this research are that there are positive and negative impacts from the use of artificial intelligence, from the process to the product of the artwork. While criticism centers on the limited imagination and creativity of artists when using artificial intelligence as a medium for creating works of art.
The Effect of Distributive, Procedural, and Interactional Justice on Organizational Cynicism Moderated by Hope

Theoretical and empirical evidence suggests that organizational justice impacts organizational cynicism, but the influence of its various dimensions is inconsistent. This inconsistency necessitates the introduction of moderating variables for a more comprehensive understanding. In this context, hope is selected as a moderating variable due to its ability to mitigate the adverse effects of organizational cynicism. The objective of this study is to explore how distributive, procedural, and interactional justice affect organizational cynicism, with hope serving as a moderating factor. The research adopts an explanatory correlational design. Participants include 126 employees from the Mojokerto State Police School (SPN), selected through census sampling. The study utilizes scales for measuring organizational cynicism, organizational justice, and hope. Data is analyzed using moderated regression analysis (MRA). The findings reveal that distributive and interactional justice negatively impact organizational cynicism. Moreover, hope moderates the relationship between procedural justice and organizational cynicism, as well as the impact of interactional justice on cynicism. Significantly, hope moderates the effects of distributive, procedural, and interactional justice on organizational cynicism, underscoring its role in enhancing the impact of these justice dimensions on cynicism. This research contributes to a nuanced understanding of the interplay between organizational justice and cynicism, highlighting the critical role of hope as a moderator.
A text security evaluation based on advanced encryption standard algorithm

This research approach analysis and examines a number of advanced encryption standard (AES) performance factors, including as encryption and decryption speed, processing resource, consumption, and resilience, to cryptanalysis attacks. The study’s findings demonstrate that AES is successful in providing high-level data security, particularly when used in the CBC (Cipher Block Chaining) operating mode. Performance is dependent on the length of the key that is utilized. Increasing the level of security through the use of longer keys may result in an increase in the amount of time needed for encryption. The experimental results show that the highest results from the data are as follows the length of the encryption time is 0.00005317 seconds, the length of the decryption time is 0.00000882 seconds, the results of BER and CER are 0, the results of entropy are 7.44237, and the results of avalanche influence are 54.86%.
The Grammar Explained in Videoblogs – The Example of the Mode congiuntivo

The aim of this contribution is to analyse the ways in which grammatical aspect, namely congiuntivo, is presented by the authors of videoblogs on the YouTube platform. The materials on this site are meant as extracurricular activities for learners of Italian LS/L2. Grammar always arouses controversy because its supporters find it necessary and fundamental in language learning, while its opponents are of the opinion that one can acquire language naturally, through communication, regardless of grammatical theory. Our aim is to observe how young authors, being digital savvy, present such grammatical aspects.
Exploring the Factors Affecting Online Trust in B2C E-Commerce Transactions in India: an Empirical Study

Purpose: The COVID-19 pandemic has led to a surge in e-commerce as millions of people were forced to stay at home and adopt digital channels for their purchases to avoid crowded supermarkets. It made the whole world look towards e-commerce as a one-stop solution for keeping markets alive. However, it came as an opportunity for digital fraudsters as a huge number of digital frauds were reported during this pandemic. Such incidents raised questions about online trust-related issues. Fake websites, insecure payment mechanisms, data theft, privacy breach, product reliability, etc., are a few of the reasons why many people are still not confident about using e-commerce platforms. When customers cannot physically touch, feel, and see the products, it becomes even more suspicious and raises serious uncertainty about the quality of the promised product and transaction setup in the e-commerce framework. Design/Methodology/Approch: In this study, primary data was collected through structured questionnaires from e-commerce website users belonging to Generation Z and analyzed using Structural Equation Modelling and Path Analysis in IBM SPSS AMOS version 24. Findings: Online Security, Online Privacy, and Website appearance were studied and found to have a significant positive impact on online trust. Online trust was also found to be a predictor of purchase intention. Online trust was also found to act as a full mediator between online security and purchase intention, online privacy, and purchase intention, and as a partial mediator between website appearance and purchase intention. Research, Practical &amp; Social Implications: The cross-sectional nature of this study makes it difficult for making inferences about causal relationships so new studies can adopt and check the utility of a longitudinal approach in this area. Furthermore, the data collected using convenience sampling had all young generation respondents, mostly college/university students. This current study takes only three antecedents of online trust with reference to a young generation; an exploratory study is needed here to find out new possible antecedents of developing online trust. Moreover, the appearance of the website is altogether a vast area to investigate for further development; very limited dimensions of the appearance of e-commerce websites are covered in this study.
Motivation, Intensity and Quality of Educational Activity of Russian Schoolchildren in Online Learning

The article analyzes the possibility of transitioning a general education school to an online learning format. The indicators are such categories as educational activity, quality of education, intensity of education, learning motives. To achieve this goal, a sociological survey of high school students in Rostov-on-Don (Russia), as well as their parents and school teachers, was conducted. The last two groups of respondents are presented in the status of experts. Based on the analysis of empirical data, the following conclusions were made. More than half of all high school students surveyed (66.6%) expressed their intention to continue the online learning experience they received during the response to the COVID-19 epidemic. However, the conjugations of willingness to study online with the categories “learning motives”, “quality of education”, “intensity of education” showed that the high motivation declared by high school students for the learning process does not correspond to their real behavior in distance lessons. The main motive for choosing online education for high school students is the convenience of this format of education. The survey showed a low degree of significance of other reasons for choosing online education. Preferences for online convenience and the desire to learn asynchronously reflect the unmanifested goal of getting out of the teacher’s control in order to reduce their educational activity. It can be assumed that this is due to the social immaturity of high school students and the lack of understanding by most of them of the value of secondary education. Based on the analyzed data, three approximately equal groups of respondents were identified. In the first group, high school students are focused on the standard school-lesson system with elements of e-learning (40% of respondents). In the second group, the advantages of online learning are articulated, which are associated with convenience and greater resource potential compared to classical learning (35% of respondents). The third group represents the interests of high school students, who are not so much interested in the format of education as the opportunity to get out of the control of the teacher and find themselves in a convenient educational environment to simulate learning activities (25% of respondents). This means that online learning format, the usefulness of which is obvious only if students have a stable cognitive activity, is unacceptable for most high school students.
Analisis Motivasi Kerja Pustakawan Open Library Telkom University dalam Menyelenggarakan Layanan Pesan-Antar

The delivery service at Telkom University's open library was developed and introduced generally since the Covid-19 pandemic which resulted in various limitations in the service and learning activities of the Telkom University academic community. This research aims to determine the motivation of librarians in providing delivery services and document delivery services at the Telkom University Open Library. The research method used is qualitative with a case study approach. Data collection techniques were carried out through interviews, observation, documentation and literature study. The results of the research show that the motivation for librarians to provide delivery services and document delivery services at Telkom University's Open Library is as a form of librarian worship, the desire to help users, a form of librarian responsibility, realizing excellent service, adequate facilities and infrastructure, as well as having a strong leadership role. provide direction and support to librarians.
Optimizing Classification Algorithms Using Soft Voting: A Case Study on Soil Fertility Dataset

Classification algorithms are crucial in developing predictive models that identify and classify soil fertility levels based on relevant attributes. However, optimizing classification algorithms presents a major challenge in enhancing the accuracy and effectiveness of these models. Therefore, this research aims to optimize the classification algorithm in soil fertility analysis using ensemble learning techniques, specifically Soft Voting Ensemble. This research method is designed to understand soil fertility levels in modern agriculture by comparing the performance of various classification algorithms and ensemble approaches. Using a dataset from the Purwodadi Department of Agriculture, this study examines the optimization of algorithm parameters such as Random Forest, Gradient Boosting, and Support Vector Machine (SVM) and the implementation of Soft Voting Ensemble. Before applying Soft Voting Ensemble, each algorithm was evaluated with the following results: Random Forest achieved an accuracy of 90.93%, precision of 91.08%, recall of 90.33%, and F1-Score of 90.70%; Gradient Boosting achieved an accuracy of 91.53%, precision of 91.19%, recall of 91.56%, and F1-Score of 91.38%; SVM achieved an accuracy of 88.91%, precision of 89.66%, recall of 87.45%, and F1-Score of 88.54%. After implementing Soft Voting Ensemble, the accuracy improved to 91.63%, with an average precision of 91.21%, recall of 91.77%, and F1-Score of 91.49%. This study divided the data into 80% for training data and 20% for testing data. These findings indicate that the Soft Voting Ensemble has the potential to enhance agricultural productivity and sustainability.
Enemy Logic

Pathfinding is happening automatically to generate our GroundEnemy path around towers, we have arrow towers, and we have our base Enemy script class to build our GroundEnemy and FlyingEnemy scripts off of. Let’s get to making our Play Button initiate the spawning of enemies, then implement our two enemy types. We’ll have our GroundEnemy following the path generated by the pathfinding, while our FlyingEnemy simply navigates from the spawn point to the leak point.
Perkembangan Library Cafe dalam Mendorong Produktivitas Pemustaka di Indonesia: Studi Literatur

Library cafes are one of the developments currently being carried out in several libraries in Indonesia. However, there are views that agree and disagree with the concept of cafes in libraries. This research aims to identify and analyze the development of library cafes in Indonesia. The development of the library cafe application is discussed based on various available literature. This research uses a qualitative approach with a research method, namely literature study. Data was obtained from relevant literature references such as books, journals, activity reports, and websites relevant to the problem theme. The analysis process is carried out on the data obtained, namely by making data reduction, data presentation, data reduction, and drawing conclusions. There are several differences in how library cafes are called, such as library cafe, libri cafe, library cafe or library cafe, literacy cafe, and library cafe. The library cafe has many programs such as book reviews, discussions, music concerts and others. The aim of developing a library cafe is to improve the quality of the library and the number of user visits. The advantages and disadvantages contained in the library cafe can be used as a basis for improving and improving library services.