Reviewerly/exHarmony huggingface dataset:
A Study of Health and Technology Use: Impact of Digital Media on Mental Health

In the era of digitalization, digital technologies and machines surpass and change daily activities. Digitalization significantly influenced daily practice, skills, and passion, significantly to the sleep cycle and work culture. It cuts human work and lowers errors. Digital media enables social interaction in a virtual world which is user-friendly, money-saving, energy efficient, and easily accessible. Likewise, social media usage has increased for communicating, learning, collaborating, and engaging in entertainment and shopping activities. Overuse of social media is associated with low work performance and other physical and mental issues such as less healthy social relationships, sleep problems, low life satisfaction, feelings of jealousy, anxiety, and depression. This study focuses on the impact on people's health due to the unrestricted use of technology. The study analyses people's physical and mental problems in association with technology. &#x0D;
StableYolo: Optimizing Image Generation for Large Language Models

AI-based image generation is bounded by system parameters and the way users define prompts. Both prompt engineering and AI tuning configuration are current open research challenges and they require a significant amount of manual effort to generate good quality images. We tackle this problem by applying evolutionary computation to Stable Diffusion, tuning both prompts and model parameters simultaneously. We guide our search process by using Yolo. Our experiments show that our system, dubbed StableYolo, significantly improves image quality (52% on average compared to the baseline), helps identify relevant words for prompts, reduces the number of GPU inference steps per image (from 100 to 45 on average), and keeps the length of the prompt short ( $$\approx $$ 7 keywords).
Pengaruh Efikasi Diri dan Literasi Digital terhadap Komitmen Profesi Guru (Studi Kasus di Sekolah Tarakanita Pluit Jakarta)

Self-efficacy or efficacy is a person's belief in their own motivation in carrying out certain tasks, including considerations of self-efficacy and confidence in information technology. The research aims to a) prove the effect of self-efficacy on the professional commitment of teachers at the Tarakanita Pluit School b) prove the influence of digital literacy on the professional commitment of teachers at the Tarakanita Pluit School c) prove the influence of self-efficacy and digital literacy on the professional commitment of teachers at the Tarakanita Pluit School . This research applies quantitative research methods with survey methods. This research is quantitative in nature and a survey uses questionnaires as a data collection tool. The research variables that will be studied are related variables, namely self-efficacy (X_1) and digital literacy (X_2) with a connecting variable, namely teacher professional commitment (Y). The results of this research are a) research states that the variables self-efficacy and digital competence are not the only variables that influence a teacher's professional commitment. b) The proportion of self-efficacy is moderate, namely only 31.6% of teachers' professional commitment, which can reduce teachers' professional commitment if educational institutions cannot foster the spirit of teachers' professional commitment, especially if the staff themselves do not have self-efficacy when working c) Low levels of digital literacy, accounting for only 16.4% of teachers' professional engagement, can hinder staff performance. The results of the study show that teacher professional involvement supported by digital literacy increases the growth of teacher professional involvement.
Balancing Accuracy and Speed in Gaze-Touch Grid Menu Selection in AR via Mapping Sub-Menus to a Hand-Held Device

Eye gaze can be a potentially fast and ergonomic method for target selection in augmented reality (AR). However, the eye-tracking accuracy of current consumer-level AR systems is limited. While state-of-the-art AR target selection techniques based on eye gaze and touch (gaze-touch), which follow the “eye gaze pre-selects, touch refines and confirms” mechanism, can significantly enhance selection accuracy, their selection speeds are usually compromised. To balance accuracy and speed in gaze-touch grid menu selection in AR, we propose the Hand-Held Sub-Menu (HHSM) technique.tou HHSM divides a grid menu into several sub-menus and maps the sub-menu pointed to by eye gaze onto the touchscreen of a hand-held device. To select a target item, the user first selects the sub-menu containing it via eye gaze and then confirms the selection on the touchscreen via a single touch action. We derived the HHSM technique’s design space and investigated it through a series of empirical studies. Through an empirical study involving 24 participants recruited from a local university, we found that HHSM can effectively balance accuracy and speed in gaze-touch grid menu selection in AR. The error rate was approximately 2%, and the completion time per selection was around 0.93 s when participants used two thumbs to interact with the touchscreen, and approximately 1.1 s when they used only one finger.
Keterampilan Berbicara pada Pembelajaran Bahasa Indonesia Siswa Kelas I SDN Bhayangkari

The purpose of this preliminary study is to explain how the teacher's role in improving the speaking skills of grade I students of SDN Bhayangkari. This research method is descriptive research. The research techniques used in this research are qualitative and quantitative. The implementation time of this introduction was carried out on October 25, 2022 until completion. This introductory study involved grade I students of SDN Bhayangkari and one teacher or homeroom teacher. The results of the observation showed that students were able to carry out speaking skills very well. From the test results, twenty students received excellent scores. The improvement of students' speaking skills has five roles of a teacher, namely the teacher motivates students' skills, the teacher as a facilitator, provides opportunities for students to perform, learning methods, and learning media that are suitable for improving students' speaking skills.
Toward Digital School: The Level of Usage, Competency and Awareness for Digital Storage Among School Administrators Pre to the Covid-19 Era

In today's rapidly changing and dynamic educational ecosystem, the Malaysian Ministry of Education emphasises digital education. Nationwide focus has been dedicated to the ICT proficiency of school administrators, as they play a pivotal role in technological leadership, in order to ensure the realisation of this objective. The sample consists of 266 school administrators who completed a questionnaire used as a research instrument. This study investigated the level of digital competence among school administrators in the southern peninsula of Malaysia. In addition, the researchers analysed the degree of cloud storage awareness and cloud storage usage across the samples. It was discovered that school administrators have a moderate degree of proficiency in cloud storage usage, use cloud storage infrequently, and have a high level of awareness regarding the significance of using cloud storage for school administration. It implies that school administrators require extensive training on the use and significance of cloud storage in school administration. However, these data were taken before the Covid-19 crisis. Covid-19 may have increased technological adoption in the global education system, necessitating more study on the status of these factors.
Peran Inteligensi dalam Proses Belajar Mengajar

Intelligence is a way of thinking in solving a problem or directing an action. In general, students who have high intelligence will boast in class because they know which intelligence is suitable for the learning process, so that students will easily success. The purpose of this research is to find out how the role of intelligence in the learning process takes place in schools. The methods used in this research are qualitative methods using library studies as well as literature studies. The result is that intelligence plays an important role in learning, namely as a form of process in increasing the potential of learners in learning. To increase the potential of the student, educators must develop the learning process creatively.
Lingkungan Positif yang Mendukung Pembelajaran

The purpose of this study is to comprehend the conducive atmosphere that facilitates learning. The study employed a library research methodology and a questionnaire using data sourced from multiple online resources, including books, encyclopedias, dictionaries, journals, documents, magazines, and other materials. A safe, encouraging, and learning-focused learning atmosphere is what makes kids feel good about their education. A supportive learning environment can not only help students grow emotionally but also make a big difference in their academic performance. Students will be able to process, learn, make errors, and learn again in a safe and comfortable setting, which will help them to effectively receive and assimilate information. An environment that is conducive to learning might be physical, emotional, and as well as sociological elements. There are many strategies to create a positive learning environment, including making the material relevant, coming up with an ethical code, rewarding good behavior, offering emotional support, setting up routines and structures, furnishing the classroom with comfortable furniture, and so on. Enhancing the learning environment can help students feel more at ease and driven to learn, which will lead to better learning results.
Deep learning framework for real-time face recognition using multimodal facial expression with feature optimization and hybrid classification

Smile detection plays key role in recognizing human facial expressions, especially in inter-personal relations. In modern biometrics and security applications, face recognition is widely used. In real-life applications, automated face recognition has always been challenging because people express themselves differently. Real-world scenarios present many challenges, such as low-quality images, temporal variations, and disguises that alter facial characteristics. Face recognition has been accomplished using deep learning frameworks, but there are still several challenges to overcome, such as expression variations and lack of training data. In these methods, margins are used to enforce intra-class compactness and inter-class discrepancy to prevent overfitting. Multimodal facial expression detection is used for real-time face recognition to solve the aforementioned problems. First, we utilizes the Prewitt edge detector for eyes, nose, mouth, and brow detection, and design a deep transfer learning model to extract deep features from face images. Next, feature optimization is done by harpy eagle search optimization (HESO) to selects optimal best among multiple features which reduce data dimensionality problem. Then, we employ a semi-region based convolutional neural network (SR-CNN) to analyzes the facial expressions from multimodalities such as eyes (blinking), nose (shapes), mouth (smiling), and brow (shapes). The analyzed facial expressions and corresponding input images are fed into the benchmark SVM classifier for face recognition. The facial expression based face recognition framework is most suited for real-time scenario like video surveillance. Finally, we validate the performance our framework using benchmark multi-modal Cohn-Kanade and real-time 5671 person images datasets. The simulation results show that the proposed framework can effectively suppress data dimensionality issues and achieve high recognition rate compare to state-of-art frameworks.
Exploring the Benefits and Challenges of Project-Based Learning in Higher Education

This study explores the benefits and challenges of Project-Based Learning (PjBL) in higher education. The research methodology employed a review study, utilizing various tools, methods, and software such as databases, citation management software, and data analysis software. The main findings of this study highlight the multifaceted benefits of PjBL in higher education, including enhanced student engagement, interdisciplinary collaboration, and authentic problem-solving. Implementing PjBL in higher education also presents challenges. These include faculty resistance and readiness, complexities in curriculum design and assessment, time and resource constraints, and the need for effective project management strategies. Strategies to address these challenges are discussed, including faculty training and support, curriculum alignment, assessment rubrics, and project management frameworks. This study’s findings can be useful in higher education institutions across various disciplines and areas, providing insights for educators, administrators, and policymakers who seek to integrate or enhance PjBL approaches in their curriculum. This study contributes to the existing knowledge on PBL in higher education by providing a comprehensive exploration of its benefits and challenges. It advances the understanding of how PjBL promotes active engagement, interdisciplinary collaboration, and authentic problem-solving, ultimately bridging the gap between theory and practice in education.
INCREASING LEARNING OF ENGLISH VOCABULARY THROUGH PICTURE FOR FIFTH GRADE SD SATHYA SAI DENPASAR

This study aims to determine the mastery of English vocabulary before and after implementing picture application for the fifth grade of SD Sathya Sai Denpasar, and to determine the supporting and hindering factors in implementing picture to increase the student mastery of English vocabulary. The method used in this study is a pre-experimental one-group pretest and post-test with a quantitative and qualitative approach (mixed method). The result of the discussion in this study indicated, that after implementing picture application in learning vocabulary, there was an increase. This was shown by the increase of students average score. Before implementing picture in English vocabulary learning, the students average score was 60. After the implementation was done the students average score became 69, which meant the increase was 9 points. There was an increase in students vocabulary, especially grouping particular nouns, understanding numbers and measurements, understanding adjectives, active verbs and to be, and using verbs and to be in writing questions and positive sentences in simple present continuous tense. In implementing the picture application to increase the students English vocabulary, there were supporting and hindering factors. The supporting factors are, the availability of picture from many sources, motivating students to learn, and getting students actively involved. The hindering factors are explaining the vocabulary spent much time, and the number of students in a class determines the focus of attention, especially for students who need more attention, because of their lack of vocabulary knowledge.
READING REMEDIATION AND ACADEMIC PROGRESS OF GRADE 5 PUPILS: BASIS FOR AN INTERVENTION PLAN

Reading brings people into contact with the culture and the values and principles adhered to by the community. It can help them develop own ideas about relationships. It includes the world of thoughts and emotions. It is said that a person who can read well, can function more effectively in everyday activities and can satisfy their emotional and intellectual needs more effectively. Without comprehension, reading per se would serve no purpose. As such this study aimed to assess the effect of the remediation activities to the academic progress of the Grade 5 pupils with the end view of proposing an intervention plan based on the findings. Results consisted of the respondents are aged 31-35, female, bachelors degree graduate, and have served for 1-10 years in teaching already. The reading remediation activities have a high extent of effect on the vocabulary development and comprehension of the materials read by the pupils. The reading remediation activities have a high extent of effect on the vocabulary development and comprehension of the materials read by the pupils. The pupils have a high satisfactory academic performance as an effect of the reading remediation activities implemented to them. There is a significant relationship between the extent of effect of reading remediation activities and the profile of the respondents. There is a significant difference between the effect of the implemented reading remediation activities to the pupils and their academic progress in English. Recommendations included that of the proposed intervention plan to be implemented to further improve the reading skills of the pupils in terms of vocabulary development and comprehension of the read materials.
Co-Design of Climate Agrometeorological Prediction System to Aid Small Scale Farmers

The Majority of farmers in Namibia are not able to access meteorological information and even where available processing requires human expertise which is rarely available. The purpose of this study is to develop an agrometeorological system to predict weather parameters that can aid small scale farmers to plan the type of crops to grow and the use of related pesticides. The aim of this study is to collect climate agrometeorological data from real-world environment using IoT sensors, pre-train the prediction model with the real-world environment dataset using IoT, to predict weather parameters such as temperature. In addition, an interactive Chatbot is developed for effective early warning of Agrometeorological conditions and the performance of the pilot prototype is evaluated. The mixed method research with the experimental approach is used for this study, both quantitative and qualitative data was collected through the use of interviews and questionnaires. An experiment is carried out to train and test the proposed model using real world dataset. Design Science Research Methodology (DSRM) was employed by designing and creating artifacts of the model and validating the results through pilot testing and the result of the study shows that predicting weather parameters such as humidity and temperature using IOT surpass other prediction methods with an accuracy rate of 98.2% with real-world environment dataset. The study further concludes that predicting weather parameter using IoT sensors provides fast, accurate and reliable real-time weather forecast. In addition, the study recommends translating the system in local languages to solve the communication barrier between the local users and the system.
Advancing Image Deblurring Performance with Combined Autoencoder and Customized Hidden Layers

This article introduces a novel approach to image deblurring by combining a Fourier autoencoder model. The proposed model effectively removes blur artifacts and restores image details by capturing frequency information using the Fourier Transform. In addition, the article presents a method to enhance deblurring by identifying optimal directions using an autoencoder model, trained on a dataset of blurry and sharp images to learn latent features for removing blur and restoring clarity. The encoded representations are used by the decoder to reconstruct a sharper version of the input image. A combination of two autoencoder models is employed, with a Convolutional Neural Network (CNN) handling the initial deblurring process and a fully connected model optimizing the deblurring parameters. This integrated approach leverages the strengths of CNNs in feature extraction and the flexibility of fully connected networks to produce higher quality, clearer images.
Scenario-Specific Verification of System Requirements Consistency via Time Modeling

Traditional requirement verification methods often fail to fully consider the modeling of time-dependent behaviors during system execution, which may lead to inconsistent system requirements, thereby compromise software reliability and correctness. To address this limitation, this paper proposes the Scenario-Specific Verification Method for System Requirements Consistency via Time Modeling. The Scenario-Specific Verification Method converts use case descriptions and problem graphs into Clock Constraint Specification Language (CCSL) constraints, transformed into clock graphs for verifying requirement consistency. The resulting constraints are solved with the Z3 solver to validate correctness, thereby enhancing system reliability. The novel time modeling approach provides techniques for verifying temporal dependencies in requirements. A case study demonstrates the efficiency and accuracy of the Scenario-Specific Verification Method in detecting inconsistencies. The main contributions include that a technique is demonstrated to formalize use case descriptions as CCSL constraints, improving on natural language and a method is presented utilizing CCSL time modeling and clock graphs to analyze requirements for consistency. Overall, an innovative solution for requirement verification using time modeling is offered by this paper, contributing to the enhancement of system development process reliability and correctness.
Coset Leaders of the First Order Reed-Muller Codes in the Classes of Niho Functions and Threshold Functions

The notion of coset leader has applications in coding theory and cryptography. It has been studied in several papers. In this paper, we extend a recent study, made on the coset leaders of the first order Reed-Muller codes, to two classes of Boolean functions which have played an important role in diverse domains of Boolean functions, and whose study was missing in this context. We characterize the coset leaders that belong to the classes of Niho functions and threshold functions (this second class being a generalization of the class of majority functions).
Context-Enhanced Language Models for Generating Multi-paper Citations

Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.