Reviewerly/exHarmony huggingface dataset:
The Effectiveness of an Educational Environment Based on Augmented Reality in Developing the Motivation for Achievement among Female Students of the University of Jeddah

In response to the demands of digital transformation in the Kingdom of Saudi Arabia, Vision 2030, which aims to improve education and provide future jobs, and depends on production, technical skills and the speed with which they are acquired, this research was conducted. The researcher used the two approaches (descriptive and quasi-experimental with an educational design based on one group with measurement (before / after) the first group test before the post-test) to achieve the objectives of the research and answer its questions. The research community consists of all female students of the College of Computer Science and Engineering in the Department of Information Systems and Technology - seventh level - at the University of Jeddah for the second semester 1440-1441 AH. The number of (35) students enrolled in the human-computer interaction course. The study found an interactive effect between an educational environment based on augmented reality and the development of the achievement motivation skill among female students of the University of Jeddah.
SemiSegSAR: A Semi-supervised Segmentation Algorithm for Ship SAR Images

&lt;p&gt;Automatic ship segmentation from high-resolution Synthetic Aperture Radar (SAR) remote sensing images has been a topic of interest that has gradually gained attention over the years due to the abundance of earth observation sensors. Recently, deep learning methods have provided a breakthrough increasing the performance greatly by using large amount of labeled data. Yet, the high cost related to the samples labeling and their scarcity result in significant limitation of their wide use. Therefore, it is crucial to overcome the unlabeled inputs challenge and develop semi-supervised learning approaches to enhance the machine learning models capacity. Our letter proposes a semi-supervised segmentation algorithm for SAR images named SemiSegSAR based on the use of Graph Signal Processing. This method includes instance segmentation; texture and statistical SAR features to represent the nodes of the graph; K-nearest neighbors to construct the graph; and Sobolev minimization algorithm to tackle the problem of semi-supervised semantic segmentation. The proposed algorithm is trained and tested using the publicly available SSDD and HRSID ship detection datasets. Experiments show that SemiSegSAR outperforms the current state-of-the-art semi-supervised and supervised methods while requiring only few labeled data.&lt;/p&gt;
Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor

The implementation of Data Mining is very much needed by UD Andar because this trading business sells various types of products. This trading business sells powdered herbal medicine, plastic bags, food &amp; beverage ingredients, and frozen foods that are in demand by consumers. Judging from the large number of consumer requests, it turns out that there are several best-selling and not-selling products, so based on the last 1 year data, a prediction of the best-selling product sales is needed, in order to make it easier for trading businesses in planning stock providers. Because the current system is still manual, the data obtained is less accurate and efficient. So to overcome this, we need a sales prediction system for best-selling products with data mining techniques using the k nearest neighbor method. This research produces a system of k nearest neighbor algorithms in data mining techniques that help to predict the sales of the best-selling products at UD Andar
Multi-path reasoning for Multi-hop Question Answering over Knowledge Graphs

In order to reduce the error transfer existing in the application of knowledge distillation network in the previous knowledge question answering model, a multi-path reasoning teacher network model is proposed. The basic reasoning model is obtained according to the idea of bidirectional reasoning, and then two sets of bidirectional reasoning answer search paths are added to improve the accuracy of the intermediate supervision signal generated by the teacher network and reduce the negative effect of the teacher network on the student network. The experimental results have proved the effectiveness of this method. In addition, the multi-path reasoning method proposed in this paper can provide ideas for knowledge reasoning, knowledge question answering and other fields.
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages

For high-resource languages like English, text classification is a well-studied task. The performance of modern NLP models easily achieves an accuracy of more than 90% in many standard datasets for text classification in English (Xie et al., 2019; Yang et al., 2019; Zaheer et al., 2020). However, text classification in low-resource languages is still challenging due to the lack of annotated data. Although methods like weak supervision and crowdsourcing can help ease the annotation bottleneck, the annotations obtained by these methods contain label noise. Models trained with label noise may not generalize well. To this end, a variety of noise-handling techniques have been proposed to alleviate the negative impact caused by the errors in the annotations (for extensive surveys see (Hedderich et al., 2021; Algan & Ulusoy, 2021)). In this work, we experiment with a group of standard noisy-handling methods on text classification tasks with noisy labels. We study both simulated noise and realistic noise induced by weak supervision. Moreover, we find task-adaptive pre-training techniques (Gururangan et al., 2020) are beneficial for learning with noisy labels.
Relations of Learners’ Attitude and Academic Performance in Online Distance Learning In Araling Panlipunan

Online learning is not a new trend in education, some of the universities and colleges have been using it. Online distance learning is one of the three learning modalities that Pulo National High School implemented this SY 2020-2021. It is a big challenge for the teachers and school administration to venture into this platform since they are used in the traditional face to face mode of instruction. Araling Panlipunan is the subject that learners either love or hate. Among the attitude problems that the teachers encountered in teaching the subject is the interest and engagement. Will the attitude and academic performance of the learners towards Araling Panlipunan changes as the mode of teaching changes? The research uses descriptive methods as means for gathering data and information and interpreting the learners’ attitude and academic performance in online learning modality. Correlational method is used to find out if there is a significant relationship between the learners’ attitude and academic performance in the subject Araling Panlipunan in the online learning modality. The findings reveal that mean scores obtain from the learners’ self-assessment attitude is 4.19 which interpreted as positive attitude. Academic achievement of the learners in the fourth grading period is at 90.47 and as classified as Advanced in the levels of proficiency. With regards to the relations of attitude and academic performance, shows that there is a negligible, positive correlation between attitude and academic performance, which is not statistically significant (r = 0.043, N =169, p &lt; 0.05). Positive attitude does not guarantee high academic performance. Since there is a minimal relation and is not statistically significant between the attitude and academic performance.
PENGARUH KETERSEDIAAN SUMBER BELAJAR DAN MINAT TERHADAP HASIL BELAJAR IPS KELAS VII SMPM 06 DAU

This research is motivated by the number of teachers and social studies textbooks as learning resources which are less than the number of students, and students' interest in participating in the social studies learning process is still lacking. As a result, students achieve learning outcomes that do not meet the specified KKM. The aims of this study were to: (1) explain the effect of the availability of learning resources on social studies learning outcomes for seventh grade students of SMP Muhammadiyah 06 Dau Malang; (2) explain the effect of interest in learning on the social studies learning outcomes of students. class VII SMP Muhammadiyah 06 Dau Malang; (3) explaining the effect of the availability of learning resources and interest in learning on social studies learning outcomes for seventh grade students of SMP Muhammadiyah 06 Dau Malang. This study uses a quantitative approach with descriptive statistics. The population and samples used were all seventh-grade students of SMP Muhammadiyah 06 Dau Malang. The data analysis technique used is multiple linear regression analysis with a probability level of 0.05. The results of the study show 1) the availability of learning resources has an influence on social studies learning outcomes; 2) interest in learning has an influence on social studies learning outcomes; 3) the availability of learning resources and interest in learning affect social studies learning outcomes with a significance level of (0.01) &lt; (0.05).&#x0D; Abstrak&#x0D; Penelitian ini dilatarbelakangi oleh jumlah guru dan buku paket IPS sebagai sumber belajar yang lebih sedikit dari jumlah siswa, dan minat siswa untuk mengikuti proses pembelajaran IPS masih kurang. Akibatnya, siswa mencapai hasil belajar yang tidak memenuhi KKM yang ditetapkan. Tujuan penelitian ini adalah untuk: (1) menjelaskan pengaruh ketersediaan sumber belajar terhadap hasil belajar IPS siswa kelas VII SMP Muhammadiyah 06 Dau Malang; (2) menjelaskan pengaruh minat belajar terhadap hasil belajar IPS siswa kelas VII SMP Muhammadiyah 06 Dau Malang; (3) menjelaskan pengaruh ketersediaan sumber belajar dan minat belajar terhadap hasil belajar IPS siswa kelas VII SMP Muhammadiyah 06 Dau Malang. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis statistik deskriptif. Populasi dan sampel yang digunakan adalah seluruh siswa kelas VII SMP Muhammadiyah 06 Dau Malang. Teknik analisis data yang digunakan adalah analisis regresi linier berganda dengan taraf probabilitas 0.05. Hasil penelitian menunjukkan 1) ketersediaan sumber belajar memberikan pengaruh terhadap hasil belajar IPS; 2) minat belajar memberikan pengaruh terhadap hasil belajar IPS; 3) ketersediaan sumber belajar dan minat belajar berpengaruh terhadap hasil belajar IPS dengan taraf signifikansi (0.01) &lt; (0,05).
Print and Electronic Resources Usage and Preference among University of Agricultural Sciences, Dharwda and University of Horticultural Sciences, Bagalkot: A Comparative Study

Study explores the extent to which Faculty and PG students in universities setting use print and electronic resources.Reading preferences and use of print and electronic resources vary among different disciplines.The purpose of this paper is to know the preferred sources of information (Electronic vs. Print) among the faculty and PG students, simple random sampling techniques, descriptive survey method was used as the research design, well designed questionnaire was used for the data collection and data has been analyzed using SPSS (Version 26.0).The study found that 64.84% from UASD and (86.66%) from UHSB. both faculty and PG students.Further, the study found that most of the respondents have positive opinion on the use of e-sources than print sources.The respondents opined that, e-sources are cheaper than print sources, through electronic sources the information is available 24/7, resources provides voluminous information and they are easy to carry.Statistical techniques were used Frequency, Percentage, Mean, Standard deviation, Chi-square Test and ANOVA and the significance values that fall below the <0.05 level are accepted..
Off-Beat Multi-Agent Reinforcement Learning

We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent, i.e., all actions have pre-set execution durations. During execution durations, the environment changes are influenced by, but not synchronised with, action execution. Such a setting is ubiquitous in many real-world problems. However, most MARL methods assume actions are executed immediately after inference, which is often unrealistic and can lead to catastrophic failure for multi-agent coordination with off-beat actions. In order to fill this gap, we develop an algorithmic framework for MARL with off-beat actions. We then propose a novel episodic memory, LeGEM, for model-free MARL algorithms. LeGEM builds agents' episodic memories by utilizing agents' individual experiences. It boosts multi-agent learning by addressing the challenging temporal credit assignment problem raised by the off-beat actions via our novel reward redistribution scheme, alleviating the issue of non-Markovian reward. We evaluate LeGEM on various multi-agent scenarios with off-beat actions, including Stag-Hunter Game, Quarry Game, Afforestation Game, and StarCraft II micromanagement tasks. Empirical results show that LeGEM significantly boosts multi-agent coordination and achieves leading performance and improved sample efficiency.
Bayesian sample size determination for causal discovery

Graphical models based on Directed Acyclic Graphs (DAGs) are widely used to answer causal questions across a variety of scientific and social disciplines. However, observational data alone cannot distinguish in general between DAGs representing the same conditional independence assertions (Markov equivalent DAGs); as a consequence the orientation of some edges in the graph remains indeterminate. Interventional data, produced by exogenous manipulations of variables in the network, enhance the process of structure learning because they allow to distinguish among equivalent DAGs, thus sharpening causal inference. Starting from an equivalence class of DAGs, a few procedures have been devised to produce a collection of variables to be manipulated in order to identify a causal DAG. Yet, these algorithmic approaches do not determine the sample size of the interventional data required to obtain a desired level of statistical accuracy. We tackle this problem from a Bayesian experimental design perspective, taking as input a sequence of target variables to be manipulated to identify edge orientation. We then propose a method to determine, at each intervention, the optimal sample size capable of producing a successful experiment based on a pre-experimental evaluation of the overall probability of substantial correct evidence.
Knowledge helps farmers wisdom village Northeast rural library sound transformation

Our college student innovation and entrepreneurship project is aimed at revitalizing the rural cultural construction and put forward the project.
SDQ: Stochastic Differentiable Quantization with Mixed Precision

In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used. In addition, as new hardware that supports mixed bitwidth arithmetic operations, recent research on mixed precision quantization (MPQ) begins to fully leverage the capacity of representation by searching optimized bitwidths for different layers and modules in a network. However, previous studies mainly search the MPQ strategy in a costly scheme using reinforcement learning, neural architecture search, etc., or simply utilize partial prior knowledge for bitwidth assignment, which might be biased and sub-optimal. In this work, we present a novel Stochastic Differentiable Quantization (SDQ) method that can automatically learn the MPQ strategy in a more flexible and globally-optimized space with smoother gradient approximation. Particularly, Differentiable Bitwidth Parameters (DBPs) are employed as the probability factors in stochastic quantization between adjacent bitwidth choices. After the optimal MPQ strategy is acquired, we further train our network with entropy-aware bin regularization and knowledge distillation. We extensively evaluate our method for several networks on different hardware (GPUs and FPGA) and datasets. SDQ outperforms all state-of-the-art mixed or single precision quantization with a lower bitwidth and is even better than the full-precision counterparts across various ResNet and MobileNet families, demonstrating the effectiveness and superiority of our method.
Teachers' language use: directions and administration conversation in classroom interaction

The intuition that happens within the classroom exercises when instructors and understudies exchange their thoughts through communication illustrates its viability. Understudies can grow their verbal capabilities when they uncover more remote dialect input by hearing the teachers use the target language in classroom interaction and practicing it when they have openings to communicate utilizing English as the target dialect. The perception appeared that instructors used target dialect more for administration conversation than for directions conversation. This research applied descriptive qualitative research. Qualitative research design aims to describe the data in more detail in a broader sentence clarification. The members of this investigation were two English teachers from one Public Islamic Senior High School in Parepare City, South Sulawesi, Indonesia. This study found that all language functions occurred in teachers 1 and 2 teaching activities in the instructional talk. Teacher 1 used more management talk in the classroom about welcoming, checking nearness, giving instruction, empowering understudies, giving commendations, inquiring about an address, and closing action. In the classroom, it was found that all the teachers spoke all language functions in the class. By analyzing the extracts by teacher 1, there are five unspoken language functions. Teacher 2 extract showed that there are seven unspoken language functions. The teachers believed that using the students' native language was critical to their comprehension. They moreover accept that the utilization of target dialect was imperative in expanding students' comprehension.
Sistem Pakar Diagnosa Penyakit Pada Perokok menggunakan Metode Teorema Naive Bayes

The purpose of this research is to produce an expert system for diagnosing disease in smokers using the Naïve Bayes Theorem method. The problem that arises in this study is the process of determining the disease in smokers through the diagnosis of experts, patients must come to the hospital and see a doctor during working hours. In the development of this expert system using the waterfall SDLC method with the stages of Analysis, Design, Coding, Testing, and testing methods carried out in this Blackbox research. To determine the type of smoker's disease, this system uses the PHP MySQL Database programming language, and Dreamweaver uses. The results of this study are in the form of a website-based expert system that is able to help users or the public in diagnosing passive smoking and providing formations about smoking diseases. The results of the Blackbox Testing test have an average score of 4.2 with a valid category
ADVANCING ENVIRONMENTAL SUSTAINABILITY THROUGH DEVELOPING ECOLOGICAL CONSCIOUSNESS IN INTERNATIONAL STUDENTS AS PART OF A PREPARATORY DEPARTMENT EDUCATIONAL PROGRAMME

The article is devoted to the problem of development of ecological consciousness in preparatory department students. The authors justify the need for implementation of environmental education in the process of training and illustrate the possibility of promoting environmental sustainability by developing ecological consciousness and ecological culture in international students through teaching various subjects.
Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures

This research &lt;span&gt;focuses on building a system to translate continuous Indonesian sign system (SIBI) gestures into text. In a continuous gesture, a signer will add an epenthesis (transitional) gesture, which is hand movement with no meaning but needed to connect the hand movement of one word with the next word in a continuous gesture. Reducing the number of irrelevant inputs to the model through automated epenthesis removal can improve the system's ability to recognize the words in continuous gestures. We implemented threshold conditional random fields (TCRF) to identify epenthesis gestures. The dataset consists of 2,255 videos representing 28 common sentences in SIBI. The translation system consists of MobileNetV2 as a feature extraction technique, removing epenthesis gestures found by the TCRF, and a long short-term memory (LSTM) for the classifier. With the MobileNetV2-TCRF-bidirectional LSTM model, the best word error rate (WER) and sentence accuracy (SAcc) were 33.4% and 16.2%, respectively. Intermediate-stage processing steps consisting of sandwiched majority voting of the TCRF and the removal of word labels whose number of frames is less than two frames, along with LSTM output grouping, were able to reduce WER from 33.4% to 3.4% and increase SAcc from 16.2% to 80.2%.&lt;/span&gt;
Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems

We develop two compression based stochastic gradient algorithms to solve a class of non-smooth strongly convex-strongly concave saddle-point problems in a decentralized setting (without a central server). Our first algorithm is a Restart-based Decentralized Proximal Stochastic Gradient method with Compression (C-RDPSG) for general stochastic settings. We provide rigorous theoretical guarantees of C-RDPSG with gradient computation complexity and communication complexity of order $\mathcal{O}( (1+\delta)^4 \frac{1}{L^2}{\kappa_f^2}\kappa_g^2 \frac{1}{\epsilon} )$, to achieve an $\epsilon$-accurate saddle-point solution, where $\delta$ denotes the compression factor, $\kappa_f$ and $\kappa_g$ denote respectively the condition numbers of objective function and communication graph, and $L$ denotes the smoothness parameter of the smooth part of the objective function. Next, we present a Decentralized Proximal Stochastic Variance Reduced Gradient algorithm with Compression (C-DPSVRG) for finite sum setting which exhibits gradient computation complexity and communication complexity of order $\mathcal{O} \left((1+\delta) \max \{\kappa_f^2, \sqrt{\delta}\kappa^2_f\kappa_g,\kappa_g \} \log\left(\frac{1}{\epsilon}\right) \right)$. Extensive numerical experiments show competitive performance of the proposed algorithms and provide support to the theoretical results obtained.
Penerapan Metode Prototipe Dalam Pembangunan Website Promosi Dusun Gedong Menggunakan CMS Wordpress

Abstract–Gedong Hamlet is one of the hamlets in the Tajuk village area, Getasan District, Semarang Regency, Central Java Province. This hamlet is located on the slopes of Mount Merbabu at an altitude of 1,700 meters above sea level. Road access to this village is quite difficult and can only be passed by one four-wheeled vehicle. This hamlet has tourism potential of amazing natural beauty and abundant produce through vegetables. However, the difficulty of access to this hamlet makes the potential of the hamlet less well known by the public. This study aims to promote the potential of Gedong hamlet so that it is known and the wider community can get to know Gedong hamlet. So with the recognition of this hamlet, it is hoped that many visitors will come to enjoy the natural potential of Gedong hamlet and the potential of agricultural products, so that it can further improve the economy of the people of Gedong hamlet. The solution used to overcome this problem is to build a website that contains a profile of Gedong hamlet, natural potentials, and market tourism packages in Gedong hamlet. Website development using CMS Wordpress which is an open source application and is supported by many plugins, and this CMS has been widely used by other large websites. The result is that orders can be immediately responded to by the manager, making it easier for both managers and prospective tourists to transact