Reviewerly/exHarmony huggingface dataset:
Pengaruh Metode Pengajaran Guru terhadap Motivasi Belajar Siswa SMP pada Pembelajaran Online

Abstract. During the Covid-19 pandemic, students' learning motivation during online learning activities has decreased somewhat. Learning motivation is a process in which a person performs an activity to learn. Various factors are estimated to affect the decline in student learning motivation during the Covid-19 pandemic, including the teacher’s teaching method. This study aims to see how much influence teacher’s teaching methods have on student learning motivation during online learning. The respondents in this study were 549 junior high school students in the city of Bandung. Respondent were obtained using Convenience Sampling. Online Teaching Method was measured using Online Teaching scale by Johnson &amp; Aragon (2003). Learning Motivation was measured by modified instrument from Ilmi Khoir Purba (2017) referring to aspects of Learning Motivation by Schunk, Pintrich, and Meece (2012). The Correlation result was 0.598 that is classified as moderate, with contribution 35.8%. The level of motivation for learning junior high school students in the city of Bandung during online learning is classified as moderate.&#x0D; Abstrak. Selama pandemi Covid-19 motivasi belajar siswa selama kegiatan pembelajaran online terbilang menurun. Terdapat berbagai faktor yang diperkirakan dapat mempengaruhi menurunnya motivasi belajar siswa selama pandemi Covid-19, salah satunya adalah metode pengajaran guru. Untuk itu penelitian ini bertujuan untuk melihat seberapa besar pengaruh metode pengajaran guru terhadap motivasi belajar siswa selama pembelajaran online. Responden dalam penelitian ini berjumlah 549 siswa, yang berstatus sebagai Siswa SMP di Kota Bandung. Data responden diperoleh menggunakan Convenience Sampling. Metode Pengajaran Online diukur menggunakan alat ukur yang mengacu pada aspek Pengajaran Online oleh Johnson &amp; Aragon (2003). Motivasi belajar diukur menggunakan hasil modifikasi alat ukur dari Ilmi Khoir Purba (2017) yang mengacu pada aspek Motivasi Belajar oleh Schunk, Pintrich, dan Meece (2012). Hasil korelasi sebesar 0.598, dimana nilai korelasi tergolong sedang, dengan nilai kontribusi sebesar 35.8%. Tingkat motivasi belajar siswa SMP di Kota Bandung selama pembelajaran online ini tergolong dalam kategori sedang.
On the Convergence of an Efficient and Robust Dynamic Neural Network Concept with Application to Solving Traveling Salesman Problems

In our previous contributions [20, 21, 22], we have clearly demonstrated that the dynamic neural network concept (DNN-concept) for solving shortest path problems (SPP) and traveling salesman problems (TSP) outperforms the best heuristic methods proposed by the literature. However, in our numerous contributions and also according to the literature, the effects of the step sizes of both “decision neurons” and “multiplier neurons” on the convergence properties of the “DNN-concept” are still not investigated. The aim of our contribution is to enrich the literature by investigating, for the first time, the convergence properties of the DNN-concept for solving traveling salesman problems. We develop a mathematical model for the efficient and robust solving the traveling salesman problem (TSP). Based on the numerical study, the convergence properties of the model developed (i.e., the DNN-concept for solving TSP) is investigated. Ranges (or windows) of variation of the parameters of the developed mathematical model are determined (identified) to ensure (guarantee) the detection of the exact TSP solution/tour. In order to validate the mathematical model developed for solving TSP, a bifurcation analysis is carried out using the developed mathematical model. Various bifurcation diagrams are obtained numerically. The bifurcation diagrams obtained reveal the ranges of variation of some key parameters of the model developed to ensure (or guarantee) the convergence of the DNN-concept to the exact TSP-solution (i.e., global minimum). Concrete examples of graphs are considered and various numerical simulations are performed as proof of concept. Finally, a comparison of the results obtained with the results published in [17]-[18] lead to a very good agreement.
Developing a Word Composition Educational Game for Young Children After the Covid-19 Pandemic

The purpose of this study is to develop a word composition educational game for young children after the Covid-19 pandemic. The game is designed to help children strengthen their word composition skills and introduce new letters and syllables related to fruits. The game uses a 2D display and is developed using the Construct 2 game engine. The development method used is the waterfall method, and the game was tested on 10 parents and teachers to validate its effectiveness in helping children learn to compose words. The results of the study showed that the use of gamification and technology in early childhood education can help to motivate children and improve their learning outcomes. The use of Construct 2 as a game engine allowed for the development of a user-friendly and visually appealing educational game. The contribution of this study is to provide an innovative solution for improving children's word composition skills in a fun and engaging way.
Effects of Simulation, Socio-Economic Profile, and Attitudes on the Academic Performance of Students in Technology and Livelihood Education (TLE)

This study investigated the effects of a cooking simulation approach on students’ academic performance in the subject Technology Livelihood Education (TLE) among fourth-year high school students of the MSU-Wao Community High School. A total of 92 secondary students were selected to participate in the study from an intact pilot sample who took the TLE subject. A semi-structured questionnaire was used to collect data in a two-group pre-test post-test quasi-experiment design. Data were analyzed using descriptive statistics, independent samples t-test, and the Pearson product moment correlation. Findings revealed that the majority of the fourth-year high school students were female, aged between 15 and 16 years old, whose parents had attained a college education and have a moderate family income, and a small family size. Students in both the simulation and convention groups have positive attitudes toward TLE. Moreover, the fathers’ level of education attainment and family size have an impact on the students’ academic performance. Their level of academic performance was proficient based on their knowledge and skills in TLE, especially in cooking lessons. The academic performance of students has significantly improved when using cooking simulation. Students in the simulation group have demonstrated better proficiency and interest in the TLE subject. The findings of the study indicate that a simulation teaching approach has a positive impact on students’ academic performance. Hence, teachers are encouraged to use simulation or actual learning activities in teaching TLE lessons to enhance students’ academic performance and stimulate their educational experience.
FORMATION OF ARTISTIC AND AESTHETIC COMPETENCE OF FUTURE TEACHERS

The article deals with some aspects of the problem of formation of artistic and aesthetic competence of future teachers of primary general education. An analysis of work practice indicates that the level of professionalism in the field of art education and the qualitative characteristics of the future teacher - aesthetic culture, aesthetic and artistic taste - do not always meet the requirements of modern society. This once again confirms not only the relevance of the study, but also the need to search for and systematize forms and methods aimed at developing the artistic and aesthetic competence of future teachers.
REVIEW OF FOREIGN EXPERIENCES ON CREATING A DIGITAL LEARNING ENVIRONMENT IN THE EDUCATIONAL SYSTEM

Today in the economically leading countries of the world, a lot of practical experience has been accumulated in creating a digital educational environment in the higher education system. While the creation of a digital learning environment in higher education is more general, it is also becoming more specific in national societies. This feature is noticeable in the creation of educational servers, platforms and teaching materials. The article describes the experience of creating a digital educational environment in the higher education system of economically leading countries.
SISTEM PENGARSIPAN DOKUMEN PURCHASE ORDER PADA PT.ENERREN TECHNOLOGIES

The purpose of this study is to find out the purchase order filing system at PT. Energy Technologies. The method of data collection using observation and interviews about how the purchase order filing system at PT. Enerren Technologies, The result is a purchase order filing system at PT. Enerren Technologies uses an alphabetical system; Standard Operating Procedures (SOP) for archiving purchase orders are written in the form of work instructions, and constraints in archiving purchase orders originating from purchase orders through applications that are archived by the IT division at PT. Energy Technologies.Tujuan penelitian ini adalah untuk mengetahui sistem pengarsipan purchase order pada PT. Enerren Technologies. Metode pengumpulan data dilakukan dengan cara observasi dan wawancara dengan menjawab permasalah tentang sistem pengarsipan purchase order di PT. Enerren Technologies, Hasil penelitian adalah sistem pengarsipan purchase order pada PT. Enerren Technologies menggunakan sistem abjad, standar operasional prosedur (SOP) pengarsipan purchase order di tuangkan dalam bentuk instruksi kerja, dan kendala dalam pengarsipan purchase order melalui aplikasi di arsip oleh divisi IT pada PT. Enerren Technologies.
Peerless? How students’ experience of synchronous online teaching can disrupt the development of relationships to peers, teachers, subject and self

&#x0D; &#x0D; &#x0D; Learning is socially constructed, influenced by the norms of the learning environment as well as the relationships within it. As higher education students continue to access their learning through online platforms during the pandemic, some find the experience stressful and intimidating. Higher education students develop four key relationships during their studies: to self, to teachers, to peers and to subject. Might the shift to online learning have impacted the development of these key relationships? And if so, could adjustments to the design of online learning help? Thematic analysis of student feedback about online learning provision (n=496) indicates peer relationships are disrupted by limited interactional opportunity during some online learning formats, and that the peer relationship plays a mediating role in the development of other key relationships. Problematic synchronous teaching formats are identified and mitigations suggested. These findings are of interest to all seeking to optimise the design and delivery of online learning.&#x0D; &#x0D; &#x0D;
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization

XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big data problems, it is crucial to deploy a secure and efficient federated XGBoost (FedXGB) model. Existing FedXGB models either have data leakage issues or are only applicable to the two-party setting with heavy communication and computation overheads. In this paper, a lossless multi-party federated XGB learning framework is proposed with a security guarantee, which reshapes the XGBoost's split criterion calculation process under a secret sharing setting and solves the leaf weight calculation problem by leveraging distributed optimization. Remarkably, a thorough analysis of model security is provided as well, and multiple numerical results showcase the superiority of the proposed FedXGB compared with the state-of-the-art models on benchmark datasets.
PENGARUH SELF-EFFICACY TERHADAP PRESTASI BELAJAR BAHASA ARAB PESERTA DIDIK DI MTS AL-IKHWAN BAUBAU

This study aims to describe the self-efficacy and learning achievement of Arabic students at Mts Al-Ikhwan Baubau, as well as to examine the effect of self-efficacy on Arabic learning achievement of students at Mts Al-Ikhwan Baubau. This research uses quantitative method with causal associative type. The population of this study were all students at Mts Al-ikhwan Baubau who were in class VIII and IX as many as 46 students, the sample in this study were all members of the population. Data collection techniques were carried out using questionnaires and tests. The collected data were analyzed using descriptive analysis and inferential analysis. The results obtained: 1) self-efficacy and Arabic learning achievement of students at Mts Al-Ikhwan Baubau are in the medium category. 2) the results of inferential statistical calculations obtained p-value &lt;α (0.008 &lt;0.05) and the regression coefficient (b) of 0.224 so that H0 is rejected and H1 is accepted, this means that self-efficacy has a positive effect on participants' Arabic learning achievement educate.
COMPARATIVE STUDY OF THINK-PAIR-SHARE TECHNIQUE ANDSMALL GROUP DISCUSSION TECHNIQUE IN WRITING DESCRIPTIVE TEXT

The objective of this research wasto find out whether there is a significant difference of the students’writing achievement after implementing Think-Pair-Share and Small Group Discussion techniques. This research used quantitative approach and the population of this research was two classes of the tenthgrade students of SMA YP Unila Bandar Lampung. The instrument was writing test and the data was analyzed through independent sample t-test. The result indicated that there was a significant difference of the student’s reading achievement after implementingthink-pair-share and small group discussiontechniques which can be seen from the value of the sig level is lower than 0.05 (0.000&lt;0.05). Then, small group discussion is more effective than think-pair-share technique in teaching writing because the mean score in small group discussion class is higher than mean score in think-pair-share class (39.2963 &gt; 18.9375).&#x0D;
KESULITAN ANAK SELAMA BELAJAR DARI RUMAH (BDR) DAN PENGENALAN ENGLISH CLUB

It has been almost a year since Covid-19 has become a world pandemic, since it was announced by the World Health Organization (WHO) on March 11, 2020. The Indonesian government even issued a Large-Scale Social Restriction (PSBB) regulation to minimize the spread of Covid-19 and its effects. PSBB is applied in all sectors of life, including the education sector. Large-scale social restrictions in the education sector are carried out by the government through the Ministry of Education and Culture by issuing Guidelines for the Implementation of Home Learning, known as BDR. The purpose of this study was to find out the various difficulties experienced by early childhood during the BDR period in the Covid-19 Era. So that effective solutions can be obtained or produced to minimize the difficulties of implementing Learning from Home so that the objectives of National Education can be achieved. English club activities are a solution to overcome the boredom experienced by children during BDR. The results showed that there were many difficulties in implementing Learning from Home. Even so, we can still find various solutions to overcome the difficulties that arise from holding BDR. In addition, the cooperation of all parties involved in educational activities, namely, teachers, parents and students as well as the Ministry of Education and Culture is the key in overcoming existing difficulties.
Learning Rewards from Linguistic Feedback

We explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive learning from language assumes a particular form of input (e.g., commands). We propose a general framework which does not make this assumption, using aspect-based sentiment analysis to decompose feedback into sentiment about the features of a Markov decision process. We then perform an analogue of inverse reinforcement learning, regressing the sentiment on the features to infer the teacher's latent reward function. To evaluate our approach, we first collect a corpus of teaching behavior in a cooperative task where both teacher and learner are human. We implement three artificial learners: sentiment-based "literal" and "pragmatic" models, and an inference network trained end-to-end to predict latent rewards. We then repeat our initial experiment and pair them with human teachers. All three successfully learn from interactive human feedback. The sentiment models outperform the inference network, with the "pragmatic" model approaching human performance. Our work thus provides insight into the information structure of naturalistic linguistic feedback as well as methods to leverage it for reinforcement learning.
Defending against audio adversarial examples based on multiple-sub-detectors

Audio adversarial examples are audio input that can deceive the speech-to-text (STT) transcription neural network technology. In this paper, we propose a new defence method based on the decision-making of multiple sub-detectors to ensure security against adversarial examples for STT transcription neural networks. This method utilises a detector for defending audio adversarial examples consisting of three different sub-detectors. Then the results of the three sub-detectors are used to make the final decision, which makes the model more robust and accurate than a single detection method. Experiments were conducted to evaluate the method using 2,000 voice sound data, including 1,000 normal voice sound data and 1,000 audio adversarial examples, created on Mozilla-implementation DeepSpeech. The results of experiments to detect audio adversarial examples confirmed that the proposed method offers better performance in protection than our previous method.
Are Distributed Ledger Technologies Ready for Smart Transportation Systems?

The aim of this paper is to understand whether Distributed Ledger Technologies (DLTs) are ready to support complex services, such as those related to Intelligent Transportation Systems (ITS). In smart transportation services, a huge amount of sensed data is generated by a multitude of vehicles. While DLTs provide very interesting features, such as immutability, traceability and verifiability of data, some doubts on the scalability and responsiveness of these technologies appear to be well-founded. We propose an architecture for ITS that resorts to DLT features. Moreover, we provide experimental results of a real test-bed over IOTA, a promising DLT for IoT. Results clearly show that, while the viability of the proposal cannot be rejected, further work is needed on the responsiveness of DLT infrastructures.
Meta-Learning with Variational Bayes

The field of meta-learning seeks to improve the ability of today's machine learning systems to adapt efficiently to small amounts of data. Typically this is accomplished by training a system with a parametrized update rule to improve a task-relevant objective based on supervision or a reward function. However, in many domains of practical interest, task data is unlabeled, or reward functions are unavailable. In this paper we introduce a new approach to address the more general problem of generative meta-learning, which we argue is an important prerequisite for obtaining human-level cognitive flexibility in artificial agents, and can benefit many practical applications along the way. Our contribution leverages the AEVB framework and mean-field variational Bayes, and creates fast-adapting latent-space generative models. At the heart of our contribution is a new result, showing that for a broad class of deep generative latent variable models, the relevant VB updates do not depend on any generative neural network. The theoretical merits of our approach are reflected in empirical experiments.
A Deep Reinforcement Learning Approach for Composing Moving IoT Services

We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT services considering quality parameters. Additionally, we develop a parallel flock-based service discovery algorithm as a ground-truth to measure the accuracy of the proposed approach. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.
Colored Noise Mechanism for Differentially Private Clustering

The goal of this paper is to propose and analyze a differentially private randomized mechanism for the $K$-means query. The goal is to ensure that the information received about the cluster-centroids is differentially private. The method consists in adding Gaussian noise with an optimum covariance. The main result of the paper is the analytical solution for the optimum covariance as a function of the database. Comparisons with the state of the art prove the efficacy of our approach.
Approximating Text-to-Pattern Hamming Distances

We revisit a fundamental problem in string matching: given a pattern of length m and a text of length n, both over an alphabet of size $\sigma$, compute the Hamming distance between the pattern and the text at every location. Several $(1+\epsilon)$-approximation algorithms have been proposed in the literature, with running time of the form $O(\epsilon^{-O(1)}n\log n\log m)$, all using fast Fourier transform (FFT). We describe a simple $(1+\epsilon)$-approximation algorithm that is faster and does not need FFT. Combining our approach with additional ideas leads to numerous new results: - We obtain the first linear-time approximation algorithm; the running time is $O(\epsilon^{-2}n)$. - We obtain a faster exact algorithm computing all Hamming distances up to a given threshold k; its running time improves previous results by logarithmic factors and is linear if $k\le\sqrt m$. - We obtain approximation algorithms with better $\epsilon$-dependence using rectangular matrix multiplication. The time-bound is $\~O(n)$ when the pattern is sufficiently long: $m\ge \epsilon^{-28}$. Previous algorithms require $\~O(\epsilon^{-1}n)$ time. - When k is not too small, we obtain a truly sublinear-time algorithm to find all locations with Hamming distance approximately (up to a constant factor) less than k, in $O((n/k^{\Omega(1)}+occ)n^{o(1)})$ time, where occ is the output size. The algorithm leads to a property tester, returning true if an exact match exists and false if the Hamming distance is more than $\delta m$ at every location, running in $\~O(\delta^{-1/3}n^{2/3}+\delta^{-1}n/m)$ time. - We obtain a streaming algorithm to report all locations with Hamming distance approximately less than k, using $\~O(\epsilon^{-2}\sqrt k)$ space. Previously, streaming algorithms were known for the exact problem with \~O(k) space or for the approximate problem with $\~O(\epsilon^{-O(1)}\sqrt m)$ space.