Reviewerly/exHarmony huggingface dataset:
Sistem Pendukung Keputusan Seleksi Tenaga Fasilitator Lapangan BSPS Menggunakan Metode Multi Factor Evaluation Process

Field facilitator workers shall guide the Self-Help Housing Stimulant Assistance Program (BSPS) recipients intensively, either in technical matters or the fund use realization process during construction terms. Guidance is given from planning, implementing a construction, and forming an assistance recipient community group that can construct decent housing independently. Accordingly, field facilitator workers are imperative to guide the BSPS recipient community. Public Works and Spatial Planning Office in Gorontalo is still selecting field facilitator workers manually, bringing on assessment errors in the selection process, e.g., selecting field facilitator workers that do not fulfill the requirement. Additionally, the sheer number of applicants overwhelms staff as they have to face off piling files of applicants. It brings about a longer selection process. As such, a systematic selection process based on the determined criteria is important. Later, it can act as a reference. It is called a decision-supporting system. Our decision-supporting system aims to help make decisions to select field facilitator workers guiding the BSPS recipient community using the Multi-Factor Evaluation Process (MFEP) method. The results point out Bambang achieved the highest final score of 87.675. Hence, MFEP in the supporting system is a recommended method to make decisions to select field facilitator workers guiding the BSPS recipient community in building decent housing.
A Study on the Design Principles of Voice Training Classes for Multicultural Elementary School Students

The purpose of this study is to present the design principles of specialized voice training classes for multicultural elementary school students to help them learn pronunciation and intonation. This was achieved by reviewing current studies and literature related to voice training, pronunciation, and intonation. Subsequently, a preliminary draft of 20 principles for specialized voice training classes for elementary school students was designed. These principles were then validated and refined through Focus Group Interviews(FGIs) with voice training experts. Through experimentation, a total of 28 principles were developed, the cause was explained step by step. Although this study could not provide quantitative verification of the tested principles, this study is meaningful for providing data that can be used to form guidelines for voice training classes for multicultural elementary school students.
Pengaruh Metode SQ4R terhadap Keterampilan Membaca Teks Eksposisi Siswa Kelas VIII SMP Negeri 3 Batang Anai

Students' reading comprehension skills are influenced by several factors, namely learning methods, student interest, and the media used by the teacher. There needs to be a method that can improve students' reading comprehension skills. This study aims to determine the significant effect of reading comprehension skills of exposition text at SMP Negeri 3 Batang Anai before and after the SQ4R method is applied. This research method is experimental research, with One Group Pretest-Posttest Design. The research population was the VIII grade students of SMP Negeri 3 Batang Anai, with the VIII grade population totaling 149, and the sample of class VII.6 with the consideration that the class was the most homogeneous because it had the lowest standard deviation, which was 12.34. The sample was drawn using purposive sampling technique. The data on reading comprehension skills of exposition texts were examined and given a score and then analyzed by t-test. The combined standard deviation of the data on reading comprehension skills of exposition texts of class VIII students of SMP Negeri 3 Batang Anai was 71.45. The results showed that because tcount&gt; ttable (3.74&gt;1.67). Based on the results of the study, it can be concluded that there is a significant difference in the reading comprehension skills of exposition texts of grade VIII students of SMP Negeri 3 Batang Anai before and after using the SQ4R method.
Where Are the Readings Behind Your Concept Maps? Annotation-driven Concept Mapping

Concept maps are widely used in education and business. They are reckoned to stimulate the generation of ideas, facilitate requirement elicitation, and serve as the first step in ontology building, and as such, they are used by a wide variety of practitioners, including designers, engineers, instructors, and others, to organize and structure knowledge. However, the rationales that drive the creation of the map’s concepts and relations are often implicit in the practitioners’ heads. These rationales should be often sought in the reading material, provided by instructors or stakeholders, and ‘processed’ by the learners or designers to deliver the concept map. This poses a traceability issue where concepts can not always be traced back to the document excerpts that originated the concepts in the first place. However, the existence of such traces is envisioned to bring two major gains. Concept-to-excerpt traceability would facilitate third-party observers (e.g., stakeholders, instructors) checking out the source of possible misunderstandings in the concept map. Excerpt-to-concept traceability would enable document reading to be strategic, i.e., framed by the concept map. This work pursues the synergy between Concept Mapping and Strategic Reading by means of highlighted annotations. This results in annotation-driven concept mapping, a process where concept maps unfold in tandem with the annotations derived from the readings. Through the interplay of annotation and mapping, learners get a headstart on both activities. As mappers, learners no longer have to resort to their reminders but text annotations provide the raw material to build up the concept and relations. As readers, learners no longer wander around the readings but concepts and relations might serve as focus drivers. This work proves the feasibility of this vision by making a popular tool for concept mapping, CmapTools, annotation-driven. A focus group (n=5) is used to anticipate its utility. The results identified two moderating variables: the level of elaboration of the reading material and the degree of abstraction of the assignment topic.
Utilization of Chatbot as Non-Print Materials for the Enhancement of Teaching Instruction in Electromagnetism in Grade 10

This study tries to evaluate the Chatbot as a non-print material to enhance the teaching instruction in electromagnetism in grade 10 students and to respond to the difficulties experienced by teachers and students in teaching electromagnetism. This study was done with selected students of San Mateo National High School involving 20 students who are currently studying Science 10. A survey was also conducted by the researchers in identifying the possible competency and topics that became the focus of this research. This research used the ADDIE model in developing and utilizing the Chatbot as the non-print instructional material for the enhancement of teaching instruction in electromagnetism in Science 10. The researchers focused on the five (5) stages of the ADDIE model which includes analysis, design, development, implementation, and evaluation. In conclusion, this research provides a reference that by utilizing chatbots as non-print instructional material, an educator will engage the online learner nurture a successful learning community, make proper use of social media platforms, and alleviate the frustration and fear that goes along with learning electromagnetism. As a result of the evaluation, a chatbot is easy to use, more on online learning, can learn faster using a chatbot, and can learn things while using Facebook and Messenger. Keywords: Chatbot, technology-based, non-print material, ADDIE model, Constructivism, Online Learning, Philippines
The Trend of Leadership Research in Higher Education: A Bibliometric Overview and Visualization

This study examines leadership research in higher education from 2008 to 2022. A total of 268 important publications were systematically retrieved and bibliometrically analyzed from the Scopus database. The present study employed Bibliometrix to analyse leadership research trends. To represent the general pattern and structure of leadership research in higher education, the final analysis incorporates bibliometric indicators, e.g., annual scientific production, most prolific journal, most prolific author, country and institutions, most influential articles, co-citation, and author’s keywords. This bibliometric investigation revealed that the US, Australia, and the UK produce the most relevant publications. It is also observed that there is still a severe manque of leadership research in higher education, and there is no appreciable growth in this field from 2008 to 2022. To give more in-depth information about the trend topics and important fields of leadership research in higher education, co-citation, and the author’s keyword analysis are also employed. The study offers valuable insight into leadership research in higher education for theoretical and practical advancements for further studies.
Smart System to Recapitulate Student Attendance on Virtual Meeting Platforms During Covid-19

Educators have problems conducting online learning, such as monitoring student attendance while presenting the material. This paper aims to predict student names who attend zoom video conferences with various lighting conditions and face angles by comparing two detection and two recognition methods. This paper proposes an intelligent system based on the use of a bot that will analyse a combination of face detection and recognition method for attendance systems using video conferencing applications to carry out online learning. The proposed system will use the best combination of two methods to recapitulate student attendance. The face detection system uses Haar Cascade and MTCNN, and the face recognition system uses ResNet and FaceNet. The tests were conducted on video zoom footage taken during online lectures. The results show that MTCNN and FaceNet get the highest accuracy, 93.23%.
Performa Regresi Ridge dan Regresi Lasso pada Data dengan Multikolinearitas

Classical regression analysis with the OLS (ordinary least square) has several assumptions. One of the assumptions is that there is no multicollinearity in the predictor variables. If multicollinearity occurs in the data, there are several other methods that can be used, including lasso regression and ridge regression. These two regression models are shrinkage methods that can shrink the regression coefficient so that the variance decreases. In this study, the performance of ridge regression and lasso regression was compared for data with multicollinearity. The result of the mean of squared errors (MSE) shows that the performance of the ridge regression is better than the lasso regression. In terms of model interpretation, lasso regression is considered superior. This is because lasso regression can shrink some coefficients to zero so that only 4 of the 9 variables used in the final model.
Penerapan Business Intelligence Untuk Menganalisa Data Gempa Bumi di Indonesia Menggunakan Tableau Public

One of the natural disasters that often occurs in Indonesia is an earthquake. This is because Indonesia's geological position is between 3 important lithospheric plates, namely the Pacific, Eurasian and Indo-Australian plates. The forces between the plates are constantly changing, dampening disturbances both on land and at sea. This study aims to focus on visualizing earthquake data in Indonesia and implementing Business Intelligence to display earthquake area data, depth and magnitude. The method of this study uses the Tableau Public platform to process earthquake datasets in Indonesia obtained through www.kaggle.com for the period 01 January 2018 to 30 September 2022. This research produces a report in the form of a dashboard that contains data visualization for the earthquake area, depth and magnitude from various regions in Indonesia that can be used to assist in decisions to be taken. Various designs for dashboards can be used in Tableau to make data easier to read and understand.
Machine-learning based VMAF prediction for HDR video content

This paper presents a methodology for predicting VMAF video quality scores for high dynamic range (HDR) video content using machine learning. To train the ML model, we are collecting a dataset of HDR and converted SDR video clips, as well as their corresponding objective video quality scores, specifically the Video Multimethod Assessment Fusion (VMAF) values. A 3D convolutional neural network (3D-CNN) model is being trained on the collected dataset. Finally, a hands-on demonstrator is developed to showcase the newly predicted HDR-VMAF metric in comparison to VMAF and other metric values for SDR content, and to conduct further validation with user testing.
Machine Learning based Spam Comments Detection on YouTube

YouTube provides only some tools for the modification of the comments in the comment section. Because of this, the volume of spam comments increasing rapidly. Using Machine Learning, the comments can be detected and prevented. There are a lot of approaches in ML to detect spam. It is often seen in applications like YouTube where people watch a lot of videos for so many purposes it can be for entertainment or learning and it provides a way for users to interact with the creators through the comment section. There exists a way where people post scam comments which are quite harmful. These comments can be dangerous and they can include links to other pages which can hack any information or data or steal any confidential details when a link is composed on that comment, in some cases it can also redirect to the page where it attracts people to earn money while playing a game and it is a scam and most people have actually lost their money by clicking on such type of links displayed via comment or messages. The purpose of this project is to detect all the spam comments that are being posted on the internet to avoid any scams or any unrelated information. In this study, Naive Bayes classification algorithm is used. The detection accuracy of this proposed system is 92.78%.
Application of Deep Reinforcement Learning to Temperature Control of a Chamber for Ultra-precision Machines

Deep reinforcement learning (RL) has attracted research interest in the manufacturing area in recent years, but real implemented applications are rarely found. This is because agents have to explore the given environments many times until they learn how to maximize the rewards for actions, which they provide to the environments. While training, random actions or exploration from agents may be disastrous in many real-world applications, and thus, people usually use computer generated simulation environments to train agents. In this paper, we present a RL experiment applied to temperature control of a chamber for ultra-precision machines. The RL agent was built in Python and PyTorch framework using a Deep Q-Network (DQN) algorithm and its action commands were sent to National Instruments (NI) hardware, which ran C codes with a sampling rate of 1 Hz. For communication between the agent and the NI data acquisition unit, a data pipeline was constructed from the subprocess module and Popen class. The agent was forced to learn temperature control while reducing the energy consumption through a reward function, which considers both temperature bounds and energy savings. Effectiveness of the RL approach to a multi-objective temperature control problem was demonstrated in this research.
An efficient fragile watermarking scheme for tamper localization in satellite images

The applications of satellite images in various fields like oceanography, agriculture, biodiversity conservation, forestry, landscape, geology, regional planning, intelligence, and warfare are growing at a burgeoning rate. As satellite images carry very sensitive information, they need a secure and verifiable transmission over the network. To address the sensitive authentication issue and making the satellite images verifiable, this paper proposes an efficient ROI-based blind fragile watermarking approach. The proposed approach bundles the capability to not only verify the integrity of the watermarked satellite image but can also localize the regions where any intentional or unintentional tampering occurred in the watermarked satellite images with more than 90% accuracy. The proposed approach also achieves a good imperceptibility between the cover and watermarked satellite image in terms of PSNR (Average 60 dB). In the proposed approach, eight dynamically evolved progenies are generated corresponding to every ROI pixel. These progenies are tightly associated with each other at the bit level. The proposed approach hosts a sequence of algorithms, which when applied to these progenies, generates an authentication bit for every corresponding ROI pixel. The generated authentication bit is then embedded into the different intensities of RONI pixels. The selection of the number of bits per pixel to embed the watermark is decided dynamically. Detailed experimental results using various subjective and objective evaluation parameters have been presented to substantiate the imperceptibility and tamper localization capabilities of our proposed approach.
Pengaruh Dana Bantuan Operasional Sekolah dan Sarana Prasarana Sekolah Terhadap Kualitas Pendidikan SMP Negeri 1 Jatinegara Kabupaten Tegal

The purpose of this study was to determine the effect of school operational assistance funds (BOS) and school infrastructure on the quality of education at SMP Negeri 1 Jatinegara. The subjects of this study were 597 students in grades 7, 8 and 9 of SMP Negeri 1 Jatinegara for the 2022/2023 academic year, with a sample of 238 students. The research method in this study is a quantitative research method, and sampling use proportional stratified random sampling. Data collection techniques in this study use a questionnaire. This study uses multiple linear regression analysis. The conclution on that BOS funds had no effect on the quality of education at SMP Negeri 1 Jantinegara. School infrastructure has a positive effect on the quality or quality of education at Jatinegara 1 Public Middle School, which can be seen from the t table which is smaller than the t count, namely 1.651 &lt; 8.050. Furthermore, school operational assistance funds and school infrastructure simultaneously have a positive influence on the quality of education at SMP Negeri 1 Jatinegara as show by the F table value which is smaller than the calculate F value, namely 3.034 &lt; 46.255.
Opportunities and Challenges in Biometric Technology

Biometric advancements have the potential to provide more robust security and authentication approaches compared to conventional password-based security measures. When compared to knowledge-based security, biometrics relies on what makes you unique rather than what you know. Biometrics offers promising opportunities to enhance security but also comes with its own set of challenges. Successful implementation requires attention to these possible flaws and a rise in user approval. This article explores the potential benefits of biometric security in an effort to ascertain how broadly adopted it is. It analyses the correlation between user approval and their opinion of the feature's usefulness and accessibility. It also examines crucial acceptability elements like social, organizational, and cost considerations. Biometric systems are a type of pattern recognition technology that may identify a person by vetting their claimed physiological and/or behavioural traits. The following concept of a biometric system can be very broad, which is problematic when thinking about the specifics of contemporary biometric applications. The use of biometrics has the potential to greatly improve upon the security of current methods of authentication and identification. The broad use of biometrics solutions, in fact, has significant ramifications for the way we think about the individual's place in society. However, certain factors and limits are taken into account in the context of the application area, as is the case with any technology. Further progress will be made as scientists investigate multiple, interconnected topics. This paper addresses technical and engineering concerns while highlighting potential applications of contemporary biometric technologies
Boolean functions and their use in symmetric cryptography

Boolean functions play a central role in the security of stream and block ciphers (in symmetric cryptography). They are functions mapping binary vectors of some length to either bits (in which case we have Boolean functions strictly speaking) or to binary vectors (of the same length or of another length, we are then speaking of vectorial Boolean functions). In the first case, they play roles in stream ciphers and in the second case, they are mainly used as substitution boxes in block ciphers. Being in each case the only nonlinear components of the ciphers, they are essential to their security. They are also the main components of the ciphers whose choice induces the speed. A tradeoff must then be found between the security and the speed of the cipher. The AES winner (used nowadays as a NIST standard for civil encryption, in particular between all banks) has been chosen because it allowed the best tradeoff. We shall describe what are the main results on them and what are the main open problems they pose currently.
The Impact of Importance-Aware Dataset Partitioning on Data-Parallel Training of Deep Neural Networks

Deep neural networks used for computer vision tasks are typically trained on datasets consisting of thousands of images, called examples. Recent studies have shown that examples in a dataset are not of equal importance for model training and can be categorized based on quantifiable measures reflecting a notion of “hardness” or “importance”. In this work, we conduct an empirical study of the impact of importance-aware partitioning of the dataset examples across workers on the performance of data-parallel training of deep neural networks. Our experiments with CIFAR-10 and CIFAR-100 image datasets show that data-parallel training with importance-aware partitioning can perform better than vanilla data-parallel training, which is oblivious to the importance of examples. More specifically, the proper choice of the importance measure, partitioning heuristic, and the number of intervals for dataset repartitioning can improve the best accuracy of the model trained for a fixed number of epochs. We conclude that the parameters related to importance-aware data-parallel training, including the importance measure, number of warmup training epochs, and others defined in the paper, may be considered as hyperparameters of data-parallel model training.
Fast image dehazing algorithm via residual scene radiance adjustment

We propose a fast image dehazing (FID) algorithm from a reverse perspective. Instead of estimating more accurate transmission or atmospheric light, our work focuses on the residual scene radiance adjustment, which can improve the quality of the dehazed results and address dim effects. First, we formulate an over-dehazed scene and the respective residual scene by pixel-wise dark channel prior. Second, the transmission map is smoothed by a threshold L1-TV filter, and the over-dehazed result is recovered. Third, the residual scene is adjusted using local color information and Retinex in the brightness domain. Finally, the dehazed scene radiance is created by fusing the over-dehazed result and the adjusted residual scene. FID does not require any training process or complex transmission and atmospheric light estimation, making this algorithm very fast and robust. Experimental results demonstrate that the proposed FID outperforms state-of-the-art dehazing algorithms regarding efficiency and dehazing effect.