Reviewerly/exHarmony huggingface dataset:
PENGARUH LINGKUNGAN KELUARGA TERHADAP PRESTASI BELAJAR SISWA SEKOLAH DASAR

The purpose of this study was to determine whether there was an influence of the family environment on the learning achievement of grade IV A students at SD Inpres Mangga Dua Merauke. This type of research is quantitative research. Respondents taken were 36 students. Data collection techniques using questionnaires and documentation. The questionnaire was used to obtain data on family environment variables and documentation was used to obtain data on student achievement. The data analysis technique used correlation test, hypothesis test (t test), and determination test. Correlation test to determine the relationship of the family environment to student achievement. Hypothesis test (t test) to determine whether there is an influence of the family environment on student achievement. Determination test to determine the influence of the family environment on student achievement. The results showed that there was a significant influence of the family environment on the learning achievement of grade IV A students at SD Inpres Mangga Dua Merauke. This is evidenced by the results of the t test where the value of t count&gt; t table (5,299&gt; 2,032). The family environment has a strong positive contribution to student achievement with a correlation value of 0.673. The magnitude of the influence of the family environment on student achievement is 45.2%.
A Comprehensive Comparative Study Between LBP and LBP Variants in Face Recognition

LBP is renowned as one of the most powerful local descriptors for texture description. The merits of LBP are monotonic gray invariance property and less complex algorithm. Therefore, LBP was deployed successfully in diverse range of applications. The success of LBP has inspired researchers to develop new LBP variants for diverse range of applications. These LBP variants achieve good results with respect to the application they were developed. After observing merits of LBP and its variants, it is found that there is need for comprehensive comparative study among these LBP-based descriptors and chose best among all descriptors. With this note, the proposed work provides comprehensive comparative study between 15 LBP-based descriptors which includes LBP and 14 LBP variants. Apart from LBP, the other 14 are HELBP, VELBP, NI-LBP, AD-LBP, DLBP, tLBP, RD-LBP, MRELBP-NI, MB-ZZLBP, MBP,6 × 6 MB-LBP, OC-LBP, LDBP and LNDBP. For all descriptors, the features are extracted globally, and then, PCA is used for feature compaction. Ultimately classification is performed by RBF, the SVMs-based method. Experiments performed on ORL face dataset confirm that among all 15, it is MB-ZZLBP which secures superior accuracy than other 14 descriptors. MB-ZZLBP also out classes numerous methods from literature.
Deconstructing barriers to support Japanese students in group discussion

Submitted: 11 March 2022 Accepted: 10 June 2022 Published online: 4 October, TAPS 2022, 7(4), 73-75 https://doi.org/10.29060/TAPS.2022-7-4/CS2783 Kiyotaka Yasui, Maham Stanyon, Yoko Moroi, Shuntaro Aoki, Megumi Yasuda, Koji Otani & Yayoi Shikama Centre for Medical Education and Career Development, Fukushima Medical University, Fukushima, Japan I. INTRODUCTION Educational strategies that are effective in one culture may […]
Peramalan Emisi Karbon Menggunakan Metode SARIMA dan LSTM

The majority of greenhouse gas (GHG) effects are caused by very high levels of carbon emissions in the world. Therefore, it is necessary to take action to control the levels of carbon emissions in the world. In this study, the world's carbon emission levels were forecasted based on time series data on carbon emissions from 1949 to 2018 in North America. This study uses 2 forecasting methods, namely SARIMA and LSTM, with the consideration that both methods are considered capable of providing good results. Forecasting results show that the best parameter for SARIMA is [(0,1,0) (1,1,0)12] with a MAPE of 1.995%. Meanwhile, if you use the LSTM method with parameters 1 input, 4 hidden layers, and output 1, it produces a MAPE of 0.540%. This condition makes the LSTM method more optimal for predicting carbon emission levels in the world.
Implementasi Algoritma Pemilihan Node Tetangga Terbaik Pada Protokol Routing DSR di Jaringan MANET

Mobile Ad Hoc Network (MANET) is a wireless telecommunications technology that consists of a collection of dynamic nodes. Due to these properties resulted in the mobility of nodes in MANET. So that every relationship between nodes will always change and cause the process of finding a vulnerable route to take a long time. Each node also has a role as a host or router in exchanging information. In MANET delivery assurance and the ability to deal with path changes on an ad hoc basis are critical. So that in sending data packets from the source node to the destination node, a Routing protocol is needed. The routing protocol Dynamic Source Routing (DSR) in the route search process broadcasts RREQ to all neighboring nodes (intermediate nodes) without knowing whether the node is the best node or not. So that the possibility of the route being interrupted because it is not composed of the best nodes can occur. A modification of the DSR routing protocol is proposed by finding the best intermediate node based on parameters, namely bandwidth, RTT, packet loss ratio. Then the neighboring nodes (intermediate nodes) that have been selected will arrange the delivery route from the source node to the destination node. The performance of the modified DSR and DSR routing protocols will then be measured using test parameters, namely throughput, packet delivery ratio (PDR) and average end to end delay. Based on the results of research trials, the application of the modified DSR method can improve the performance of the DSR protocol. Which after the application of the method, the average throughput increased by 25%, Packet Delivery Ratio (PDR) was 1.11% and the average end-to-end delay decreased by 17.64 %.
Evaluasi Tata Kelola Teknologi Informasi pada Proses Pengelolaan Inovasi dan Pengelolaan Perubahan Teknologi Informasi Menggunakan COBIT 2019 di PT. XYZ

The development of information technology (IT) is a major factor in driving the business transformation process in an organization. To drive the business transformation process, good IT governance is essential, so that the utilization of IT in organizational functions can run optimally. This study aims to measure the level of IT governance capability at PT.XYZ. The IT governance audit conducted in this study used the COBIT 2019 framework in mapping the process to be assessed. In the design factor results, it is found that there are 2 domain processes that have a value above 75%, namely APO04 and BAI06 which are then obtained in the process of measuring the capability of each level, namely APO04 has a capability value at level 1, and BAI06 has a level 1 capability value. carried out the results of the gap analysis and obtained an average value of the gap of 3, that PT.XYZ needs to improve IT governance and management in order to achieve organizational goals. Based on these improvements, several recommendations are given for improvements that can be made by the company.
Sistem Monitoring Kondisi Kesehatan Sebelum dan Sesudah Olahraga Menggunakan Pulse Sensor dan Sensor DS18B20 dengan Metode Naive Bayes

Heart rate and body temperature are essential factors that must be considered before exercise is essential to optimize exercise and know the body's condition. The trainer conducts a health check, and management evaluates the exercise results conducted on the athlete. The Internet of Things is the solution provided to facilitate the monitoring of health conditions and measure exercise intensity. Before and after exercise, the health condition monitoring system is designed to determine the user's health condition by measuring heart rate and body temperature as parameters for decision-making by applying the Naïve Bayes method. The Naive Bayes method is rule-based with the python sci-kit-learn programming language and data communication with MQTT. From the study results, the Sports Health condition monitoring system has succeeded in providing monitoring results, condition prediction decisions, and the accuracy of the Naïve Bayes method by 75%.
Development of Blended Learning Instruction Materials in NCS-based Home Appliance Hardware Development Subject

Objectives The purpose of this study is to develop blended learning instruction materials for ‘Detailed circuit diagram drawing’ section using Google Classroom in the NCS-based home appliance hardware development subject, and to investigate the effect on the academic achievement of specialized high school students. Methods In this study, the development direction and learning goals of class materials were set through the analysis of curriculum, job duties, and learners, and a plan for using Google Classroom was devised. Instruction materials that can be applied to specialized high school practical classes were developed, Based on the NCS learning module, this class material presented practical information, relational knowledge, design drawings, and self-checklist, and was developed to enable learners to learn by level and self-directed learning. In this study, academic achievement was evaluated by applying the heterogeneous control group after test to two classes of technical high schools. Results As a result of analyzing the effect of the instructional materials developed in this study on academic achievement, there was a significant improvement in academic achievement only in the subgroup in the cognitive domain. On the other hand, in the psychodynamic domain, the academic achievement of the median and subgroups improved, and in the affective domain, there was a significant effect on the students in the middle group. In all areas of academic achievement, there was no significant effect for the students in the upper group. Conclusions The blended learning instructional material developed in this study ‘Create a detailed circuit diagram’ is more effective for the middle-class and lower-class students in terms of academic achievement compared to the traditional instruction-oriented classes.&#x0D;
Analysis of Korean Middle School Students' Perception of ICT: Focusing on ICILS 2018

Objectives In this study, affective factors such as self-efficacy, perception, and career expectations for Information and Communication Technology (ICT), which were surveyed in the ‘International Computer and Information Literacy Study (ICILS) 2018’, were analyzed. In particular, we focused on the effects of student characteristics such as Computer and Information Literacy(CIL) level, gender, and computer/software learning experience. Furthermore, these data were used to identify the ICT-related characteristics of middle school students in South Korea by identifying the similarities and differences with other participating countries through international comparison. Methods A total of 2,875 students (1,497 male (52.1%) and 1,378 female (47.9%)) enrolled in the second year of 150 middle schools nationwide participated in the computer-based assessment. Computer and Information Literacy was measured through self-report data. The information of educational use of ICT and student characteristics (gender, age), and student attitudes and perceptions were collected and analyzed. Results The percentage of students who responded that they were able to perform well on ICT-based learning tasks was lower than the ICILS 2018 average. In particular, ‘database creation’ showed the lowest rate. Also, students' self-efficacy for general and specialized applications was lower than the ICILS 2018 average. The perceptions of the positive and negative effects of ICT on society were higher than other participating countries. In terms of expectations for ICT-related careers, male students showed higher expectations than female students, indicating a gender difference. Conclusions Implications for effective approaches for nurturing future digital talents that can maximize the effect of education by improving the quality of computer and information education and reducing the gap between learners were proposed.&#x0D;
A Study on the Effects of Teaching and Learning on High School Hair Beauty Using Flipped Learning: Focusing on Basic Hair Perm

Objectives The purpose of the high school hair and beauty curriculum is to investigate the effectiveness of teaching and learning (writing, practical skills) such as self-directed learning ability, learning motivation, learning satisfaction, and learning persistence after applying flipped learning classes to basic hair perm skill classes. Methods This study divided 88 third graders of A High School in Seoul into an experimental group applying flipped learning and a control group applying lecture methods, and compared self-directed learning ability, learning motivation, learning satisfaction, and academic achievement between the two groups. Results This study divided 88 third graders of A High School in Seoul into an experimental group applied with flipped learning and a control group applied with a lecture method, and compared self-directed learning ability, learning motivation, learning satisfaction, and academic achievement between the two groups. As a result, compared to the control group to which the flipped learning class was applied, the self-directed learning ability, cognitive control, and attention concentration and satisfaction among learning motivation were significantly higher due to the induction of pre-learning video, discussion, and cooperative learning. In addition, beyond the interaction and time and space constraints between instructors and fellow learners, satisfaction with teaching behavior, satisfaction with beauty environment, and satisfaction with beauty ability were significantly higher among learning continuity and learning satisfaction. Conducting metacognitive thinking through discussion and cooperative learning, understanding and applying academic achievement (writing) was significantly increased. Conclusions Flipped learning classes have a positive effect of improving cognitive regulation, learning motivation, learning satisfaction, and learning continuity among self-directed learning abilities. Accordingly, it suggests the need to expand classes using flipped learning in the operation of high school beauty courses, and suggests the need for program development and teacher training so that they can be operated and used in various beauty subjects in the future.&#x0D;
Intelligent System Application in Clinical Management of Medical Teaching Based on Deep Reinforcement Learning

When dealing with engineering projects, there are many kinds of planning schemes. There are many problems to be solved in the performance and economic conditions of these indicators and engineering contents we study. Recently, many researchers have made great achievements in optimizing various subject projects. One of its purposes is to optimize and improve intelligent multi-objectives to make it more effective. Therefore, it is of great significance to develop intelligent multi-objective projects in academia and more engineering fields. The purpose of this study is to make efforts to strengthen the learning and construction of rapidly developing multi-objective optimization programs, and closely link these programs with neural network, fuzzy technology, interactive technology, and give a lot of examples of multi-objective optimization improvement program methods, which can eliminate the uncertainty in the multi-objective project plan, The purpose is to solve some difficult problems of multi-objective optimization and give the best suggestions. In this study, the strengthening of learning construction is combined with the clinical experiment, and most of this teaching method is put in the clinical management experiment of medical teaching. At the end of this study, we compared the performance of deep reinforcement learning with its dual structure version and interleaved structure version in different medical teaching clinical management environments and make a detailed analysis. Deep reinforcement learning provides a new idea for the improvement of intelligent multi-objective optimization and clinical management of medical teaching.
Quantifying the Unknown: Impact of Segmentation Uncertainty on Image-based Simulations.

Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.
Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data

Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain). The task is to embed both datasets red into a common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task. DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing the single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains.
Multi-Message Private Information Retrieval: A Scalar Linear Solution

In recent years, the Multi-message Private Information Retrieval (MPIR) problem has received significant attention from the research community. In this problem, a user wants to privately retrieve $D$ messages out of $K$ messages whose identical copies are stored on $N$ remote servers, while maximizing the download rate. The MPIR schemes can find applications in many practical scenarios and can serve as an important building block for private computation and private machine learning applications. The existing solutions for MPIR require a large degree of subpacketization, which can result in large overheads, high complexity, and impose constraints on the system parameters. These factors can limit practical applications of the existing solutions. In this paper, we present a methodology for the design of scalar-linear MPIR schemes. Such schemes are easy to implement in practical systems as they do not require partitioning of messages into smaller size sub-messages and do not impose any constraints on the minimum required size of the messages. Focusing on the case of $N=D+1$, we show that when $D$ divides $K$, our scheme achieves the capacity, where the capacity is defined as the maximum achievable download rate. When the divisibility condition does not hold, the performance of our scheme is the same or within a small additive margin compared to the best known scheme that requires a high degree of subpacketization.
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images

We propose a new hybrid system for automatically generating and training quantum-inspired classifiers on grayscale images by using multiobjective genetic algorithms. We define a dynamic fitness function to obtain the smallest possible circuit and highest accuracy on unseen data, ensuring that the proposed technique is generalizable and robust. We minimize the complexity of the generated circuits in terms of the number of entanglement gates by penalizing their appearance. We reduce the size of the images with two dimensionality reduction approaches: principal component analysis (PCA), which is encoded in the individual for optimization purpose, and a small convolutional autoencoder (CAE). These two methods are compared with one another and with a classical nonlinear approach to understand their behaviors and to ensure that the classification ability is due to the quantum circuit and not the preprocessing technique used for dimensionality reduction.
gOTzilla: Efficient Disjunctive Zero-Knowledge Proofs from MPC in the Head, with Application to Proofs of Assets in Cryptocurrencies

We present gOTzilla, a protocol for interactive zero-knowledge proofs for very large disjunctive statements of the following format: given publicly known circuit C, and set of values Y = {y1 , . . . , yn }, prove knowledge of a witness x such that C(x) = y1 ∨ C(x) = y2 ∨ · · · ∨ C(x) = yn . These type of statements are extremely important for the proof of assets (PoA) problem in cryptocurrencies where a prover wants to prove the knowledge of a secret key sk that associates with the hash of a public key H(pk) posted on the ledger. We note that the size of n in popular cryptocurrencies, such as Bitcoin, is estimated to 80 million. For the construction of gOTzilla, we start by observing that if we restructure the proof statement to an equivalent of proving knowledge of (x, y) such that (C(x) = y) ∧ (y = y1 ∨ · · · ∨ y = yn )), then we can reduce the disjunction of equalities to 1-out-of-N oblivious transfer (OT). Our overall protocol is based on the MPC in the head (MPCitH) paradigm. We additionally provide a concrete, efficient extension of our protocol for the case where C combines algebraic and non-algebraic statements (which is the case in the PoA application). We achieve an asymptotic communication cost of O(log n) plus the proof size of the underlying MPCitH protocol. While related work has similar asymptotic complexity, our approach results in concrete performance improvements. We implement our protocol and provide benchmarks. Concretely, for a set of size 1 million entries, the total run-time of our protocol is 14.89 seconds using 48 threads, with 6.18 MB total communication, which is about 4x faster compared to the state of the art when considering a disjunctive statement with algebraic and non-algebraic elements.