Reviewerly/exHarmony huggingface dataset:
MEDIA LITERACY FOR CIVIL COMPETENCE: THE POSITIVE EXPERIENCE OF ISRAEL

The experience of Israel in terms of introducing media literacy into the education system, primarily school education, is studied. The main stages of improving educational programmes in media literacy and achievements at each stage are determined. The main directions of transformation of modern media education in Israel are shown. Relationships between civil education and media education have been established. The problem of media competence formation in Ukrainian educational institutions is raised. Attention is focused on the important role of media education in the formation of social and civil competences of young people.
Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation Tasks

Deep neural networks are well known for demanding large amounts of training data, motivating the appearance of multiple synthetic datasets covering multiple domains. However, synthetic datasets have not yet outperformed real data for autonomous driving applications, particularly for semantic segmentation tasks. Thus, a deeper comprehension about how the parameters involved in synthetic data generation could help in creating better synthetic datasets. This work provides a summary review of prior research covering how image noise, camera noise and rendering photorealism could affect learning tasks. Furthermore, we presents novel experiments aimed at advancing our understanding around generating synthetic data for autonomous driving neural networks aimed at semantic segmentation
Competency in Seeking Clarifications in Oral Interactions: Learners’ Perceptions on Effectiveness of a Teaching Module

Fresh graduates ought to be competent in using English due to the fact that the language has pervasive influences both in commerce and within the education system in this country. However, communicating in English could be a challenge to many ESL speakers. This scenario has motivated the development of a teaching module, aimed at enhancing learners’ communicative competency in seeking clarifications as well as their confidence level when using English. Now that the module has been used by students in one public university in the country, the effectiveness of it as perceived by the learners was yet to be examined. The data were then collected through online questionnaires administered in two stages: before and after the use of the teaching module. Based on responses given by 135 respondents, the mean scores and standard deviation for the items in the questionnaire were calculated on the Google Form. The results showed some improvements in learners’ competency in using English. Based on the conclusion drawn from this study, recommendations for future research are offered.
First-person shooter (FPS) Video Game Using Ray Casting Algorithm

A personal computer has evolved where it can be used to perform various tasks such as 3D design, video rendering, and other similar activities. Video games are not an exception, and many individuals are turning to YouTube or Twitch streaming to earn money while playing video games on their computers. Developing a video game is not a simple task since the game's developer requires specific expertise to produce them. Without this knowledge, the developer would be unable to develop the video game. This project focuses on the application of the ray-casting algorithm. Ray casting is one of the algorithms that is frequently utilised in the construction of video games, particularly in shooting games. This is because the algorithm is straightforward and does not necessitate the use of high-end technology to conduct the ray casting algorithm. It will be necessary to construct a 3D shooting game for the ray casting technique to function well since this will allow for easier analysis of the accuracy of the bullet hitbox. This project demonstrates how ray casting works based on all the results made while developing the game prototype. This project focuses on becoming an example for other video game developers to develop video games using the ray casting algorithm.
Knowledge maps for large‐scale group decision making in social media content analysis

Abstract Large‐scale group decision making in social media content analysis, empowered by Artificial Intelligence tools, offers valuable insights in management research. The purpose of this paper is to identify and interpret the knowledge maps that emerge in social media research, based on deep learning algorithms and neural networks' impact in social content analytics. We conducted qualitative research, by analysing relevant articles focused on the role played by deep learning and neural networks in social media content analysis. The key themes retrieved from the selected articles have been included in visual representations, provided by Nvivo12 software, considering its text‐mining capabilities. We are aware that large‐scale group decision making (LSGDM) offers solutions for research issues on social media content analysis. This qualitative research highlights the value of LSGDM skills for researchers that developed an AI platform prototype for social media content analysis.
Exploring The Readiness of Google Slides Pedagogical Practices: Perspective of Primary Teachers

This study explores the readiness of Google Slides pedagogical practices regarding knowledge, skill, and attitude levels among primary teachers. The study was conducted using an online questionnaire to collect data. This research has utilised questionnaires as the research instrument. The sampling method used in this research is stratified random sampling. The quantitative method has been used in the study. A total of 40 respondents from Primary school teachers were selected through a simple random sampling method to answer the questionnaire. The findings indicated that the teacher's knowledge and skill levels are high among the teachers. Moreover, teachers' attitude towards using Google Slides in teaching and learning was positive. Inferential analysis using the T-test was conducted to compare the differences in the knowledge, skill, and attitude levels towards gender. The inference analysis revealed significant differences in knowledge levels associated with the teacher's gender. In contrast, there were no significant differences in skill levels and attitudes related to teacher gender when using Google Slides. Hence, this study contributes to the significant empirical results on the readiness of Google Slides pedagogical practices among primary teachers. The implications of this study are expected to contribute to government, stakeholders, teachers, students, school administrators, and educational organisations, especially practical and theoretical implications regarding teachers' readiness in terms of knowledge, skill, and attitude toward using Google Slides in teaching and learning.
A Motion-Simulation Platform to Generate Synthetic Motion Data for Computer Vision Tasks

We developed the Motion-Simulation Platform, a platform running within a game engine that is able to extract both RGB imagery and the corresponding intrinsic motion data (i.e., motion field). This is useful for motion-related computer vision tasks where large amounts of intrinsic motion data are required to train a model. We describe the implementation and design details of the Motion-Simulation Platform. The platform is extendable, such that any scene developed within the game engine is able to take advantage of the motion data extraction tools. We also provide both user and AI-bot controlled navigation, enabling user-driven input and mass automation of motion data collection.
Focus Range: Production Ray Tracing of Depth of Field

Defocus blur adds realism to computer generated images by modelling the restricted focus range of physical camera lenses. It allows artists to draw the viewer’s attention to in-focus regions of the image by separating them from the background. The widely used thin lens model provides a simple approach to achieve this effect, allowing artists to control the frame’s appearance through the distance to the focus plane and the aperture size. Our proposed focus range model extends the focus distance to a range. This allows artists to keep characters fully in focus and independently define the out-of-focus blur. We demonstrate how to achieve this robustly in the presence of specular BSDFs, ray-oriented geometry and multiple light bounces. Additionally, we share our practical experience of integrating this model into our production renderer Glimpse.
Multi-client distributed blind quantum computation with the Qline architecture

Universal blind quantum computing allows users with minimal quantum resources to delegate a quantum computation to a remote quantum server, while keeping intrinsically hidden input, algorithm, and outcome. State-of-art experimental demonstrations of such a protocol have only involved one client. However, an increasing number of multi-party algorithms, e.g. federated machine learning, require the collaboration of multiple clients to carry out a given joint computation. In this work, we propose and experimentally demonstrate a lightweight multi-client blind quantum computation protocol based on a recently proposed linear quantum network configuration (Qline). Our protocol originality resides in three main strengths: scalability, since we eliminate the need for each client to have its own trusted source or measurement device, low-loss, by optimizing the orchestration of classical communication between each client and server through fast classical electronic control, and compatibility with distributed architectures while remaining intact even against correlated attacks of server nodes and malicious clients.
Carducci e il mondo tedesco. Bilanci e prospettive

The essay takes a systematic look at some perspectives on the study of Giosue Carducci’s relations with German culture, offering a review of the research until today, examples of case studies, and a reflection on possible future research perspectives. Among the various areas addressed is that of the study of Carducci’s translations from German, with remarks on Carducci’s role in the transfer processes between Italy and Germany. Among other things, emphasis is given to the relevance of investigations into translations not yet published in the National Edition of Carducci’s Works. The last paragraph of the essay gives an account of a first census of Carducci’s autographs that have become part of German, Austrian and Swiss collections. In particular, prominence is given to autographs now preserved at the Fondation Martin Bodmer (Cologny, Genève): a manuscript of the ode Su Monte Mario (whose traces had been completely lost) and a still-unknown autograph of the poem Per la proclamazione del regno d’Italia.
Crowd Monitoring System Using Facial Recognition

The World Health Organization (WHO) suggests social isolation as a remedy to lessen the transmission of COVID-19 in public areas. Most countries and national health authorities have established the 2-m physical distance as a required safety measure in shopping malls, schools, and other covered locations. In this study, we use standard CCTV security cameras to create an automated system for people detecting crowds in indoor and outdoor settings. Popular computer vision algorithms and the CNN model are implemented to build up the system and a comparative study is performed with algorithms like Support Vector Machine and KNN algorithm. The created model is a general and precise people tracking and identifying the solution that may be used in a wide range of other study areas where the focus is on person detection, including autonomous cars, anomaly detection, crowd analysis, and many more.
Virtualization in Cloud Computing

Abstract: Virtualization and cloud computing have been two popular avenues of research over the past few years. They enable users to use applications on internet and intranet. In this research paper, we will discuss about virtualization in cloud computing in detail: its evolution, its need and benefits, challenges its facing nowadays and how to overcome them. We also discuss about cloud management and the future trends in virtualization technology and cloud computing
The Impact of Shadow Reading Techniques on Enhancing Reading Fluency among First-Year Students: A Classroom-Based Intervention Study

The study employed a quantitative research design to explore the current reading fluency of the first year students at Dai Nam University in Hanoi, Vietnam. The participants consisted of 123 first year student who have completed their first semester of English study. The findings showed that the reading fluency was at low level; M=2.74. The findings demonstrate that shadow reading techniques have a positive effect on the reading fluency of first-year students. This improvement is evident in their ability to read words accurately and quickly, maintain a consistent reading pace, and exhibit appropriate expression and intonation. As a result of improved fluency, students are better equipped to comprehend and understand the text they read. This has implications for their overall reading achievement and academic success. The study reveals that shadow reading not only enhances fluency but also increases students' motivation and engagement with reading. Students who participated in shadow reading activities reported a greater enjoyment of reading. Future investigations may delve deeper into specific factors influencing the effectiveness of shadow reading, explore its long-term impact on reading achievement, and examine variations in effectiveness among diverse student populations.
Image Blending Algorithm with Automatic Mask Generation

In recent years, image blending has gained popularity for its ability to create visually stunning content. However, the current image blending algorithms mainly have the following problems: manually creating image blending masks requires a lot of manpower and material resources; image blending algorithms cannot effectively solve the problems of brightness distortion and low resolution. To this end, we propose a new image blending method with automatic mask generation: it combines semantic object detection and segmentation with mask generation to achieve deep blended images, while based on our proposed new saturation loss and two-stage iteration of the PAN algorithm to fix brightness distortion and low-resolution issues. Results on publicly available datasets show that our method outperforms other classical image blending algorithms on various performance metrics including PSNR and SSIM.
Granular Linguistic Model Based Multimodal Data Integration for Automated Evaluation of Core Soft Skills

This research introduces an innovative hybrid intelligence framework leveraging multimodal data to automate the evaluation of core soft skills, including decision-making, conflict resolution, and creativity. The model applies the principles of the Granular Linguistic Model of Phenomena (GLMP), a sophisticated method that delineates phenomena at varying granularity levels, ensuring a detailed analysis of the exhibited skills. The process involves mining significant behavioural features from diverse data sources, specifically video, audio, and text, employing deep learning algorithms. The extracted features are then subjected to the GLMP, representing the students’ behaviour in a structured, interpretable format across multiple granularities. The GLMP application yields an exhaustive set of granular linguistic prompts that encapsulate the complexity of the identified soft skills. This multimodal information feeds into a fuzzy logic-based detector that evaluates the defined soft skills. This integrative approach merges granular linguistic modelling with multimodal data, enabling a comprehensive and accessible understanding of the students’ soft skills. The implications of this approach extend beyond the academic sphere, finding utility in broader contexts such as college admissions and job recruitment, where objective skill evaluation is crucial. This research underscores the value of multimodal integration within the GLMP framework, highlighting its critical role in translating raw data into actionable insights. It further illuminates the potential of such methods in enhancing real-world decision-making processes and outcomes.
Modeling Online Adaptive Navigation in Virtual Environments Based on PID Control

It is well known that locomotion-dominated navigation tasks may highly provoke cybersickness effects. Past research has proposed numerous approaches to tackle this issue based on offline considerations. In this work, a novel approach to mitigate cybersickness is presented based on online adaptive navigation. Considering the Proportional-Integral-Derivative (PID) control method, we proposed a mathematical model for online adaptive navigation parametrized with several parameters, taking as input the users’ electro-dermal activity (EDA), an efficient indicator to measure the cybersickness level, and providing as output adapted navigation accelerations. Therefore, minimizing the cybersickness level is regarded as an argument optimization problem: find the PID model parameters which can reduce the severity of cybersickness. User studies were organized to collect non-adapted navigation accelerations and the corresponding EDA signals. A deep neural network was then formulated to learn the correlation between EDA and navigation accelerations. The hyperparameters of the network were obtained through the Optuna open-source framework. To validate the performance of the optimized online adaptive navigation developed through the PID control, we performed an analysis in a simulated user study based on the pre-trained deep neural network. Results indicate a significant reduction of cybersickness in terms of EDA signal analysis and motion sickness dose value. This is a pioneering work which presented a systematic strategy for adaptive navigation settings from a theoretical point.
Student’s Difficulties in Using Transition Signals in Writing Descriptive Paragraph of The Second Year Students of SMP Gajah Mada Medan

This study attempt to discuss student’s difficulties use transition signals when writing descriptive paragraph of SMP Gajah Mada Medan sophomores. The subject of the research is to know the types of transition signals that are difficult for the students to use in writing descriptive paragraphs. The researcher wants to discover the dominant types of transitional signs that are difficult for students to use when writing descriptive paragraphs. The population for the research was 18 second-years SMP Gajah Mada Medan students. The study used descriptive qualitative research. The result of the research showed that there were four types of students’ difficulties in using transition cues when writing descriptive paragraphs, namely: additional idea, opposite idea or contrast, example and cause effect. The dominant type of transition signals that are difficult for students to use when writing descriptive paragraphs was cause and effect with 9 cases (33,3%). This thesis is suggested to students and teachers of the English department as a source of valuable knowledge and information on students’ difficulties in using transition signals.
Improving the Students’ Vocabulary Mastery by Using Word Cloud at The Tenth Grade of SMK Negeri 1 Gunungsitoli In 2022/2023

Vocabulary mastery is a skill that refers to a high level of proficiency in processing words in a language. The students at the tenth grade of SMK Negeri 1 Gunungsitoli lacked vocabulary mastery, so they found it difficulty to understand texts properly and did not achieve the minimum competency criteria (MMC) that has been set at school. The purpose of this study was to improve the students' ability to vocabulary mastery. In improving students' vocabulary mastery, the researcher used Word Cloud and applied it in Classroom Action Research (CAR). This research was conducted in Cycle II, there were four steps in conducting research, namely: planning, action, observation and reflection. . In Cycle I, there was no student who was classified in "very good level", there were two students (5.72%) in "good level". Instead of that, there were 19 students (54.28%) in "fair level", 14 students (40%) in "poor level". In addition, the average of the students' mark was 53.00. The results show that many students were at enough and less levels so it really needed to be improved in the next cycle. In Cycle II, there was 1 student (2.86 ) in “poor level”, there were 7 students (20%) in “fair level”, there were 6 students (17.14%) in “good level”, and 22 students ( 62.86 % ) in “very good level”. In addition, the average of the students' mark was 86.42. The students' average mark was higher than the MMC that was 75. Based on the explanation above, it is concluded that the Word Cloud can improve the students' vocabulary mastery at the tenth grade of SMK Negeri 1 Gunungsitoli in 2022/2023.&#x0D; Keywords: Vocabulary Mastery, Word Cloud, CAR
A STYLISTIC ANALYSIS OF ADVERTISING DISCOURSE: A CASE STUDY OF COKE COMMERCIAL ADVERTISEMENT

This study attempts to examine the many linguistic idioms employed in English-language commercial and coke advertisements on the internet. By gathering five coke advertisements from the internet and using them as documentation, this research employed a qualitative descriptive methodology. Data condensing, data visualization, and conclusion/verification drawing are some of the data analysis techniques employed. As a result of this research, the researcher discovered three language styles used in analyzing the data: comparison, problem solution, hard sell, soft sell, lecture and drama, straightforward style, demonstration, and slice of life. The researchers also discovered three language styles used in coke product advertisements comparison, problem solution, hard sell.
Find Drivable Segments from Road Image using Depth and RGB Image

Perception is a very critical and challenging task in the realm of autonomous driving. The current approach relies on a sophisticated model pipeline built upon various deep learning models, each tasked with solving distinct challenges. This leads to a large model size. The proposed approach dissects an entire driving scene into two distinct elements: driving backgrounds that are not for vehicles to drive onto and road segments that are for vehicles driving. It performs the pointwise fusion using disparity image and RGB image. It uses pointwise and depthwise convolution to reduce multiplication times. It integrates image segmentation neural networks Deeplab V3 as backbone and significantly reduces the model size using ResNet-18. The efficacy of the proposed neural network is substantiated through validation using the Cityscape dataset, yielding an impressive 0.979 accuracy, 0.948 precision, and a 0.947 F1-score. Furthermore, it boasts a training speed that is five times faster compared to conventional UNetbased models, and the model size is eight times smaller than UNet-based models.
MVCAL: Multi View Clustering for Active Learning

Various active learning methods with ingenious sampling strategies have been proposed to solve the lack of labeled samples in supervised learning, but most are designed for specific tasks. In this paper, we propose a simple but task-agnostic active sampling method. We introduce ‘multi-view clustering module’ to extract multiple feature maps at different levels for unsupervised clustering. According to clustering distribution, we calculate consistency, representativeness and stability to guide sampling and training. Among them, consistency measures the similarity between clustering results of two views, representativeness reflects the distance between a sample and the corresponding cluster center, and stability reflects the model’s feature representation and recognition ability for the same sample. Our method does not depend on the specific network, and can be constructed as a two-stage sampling module to supplement the existing sampling algorithm. Experiments results on image classification and object detection tasks show that our method can further enhance the effect of active learning on the basis of baseline methods.