Reviewerly/exHarmony huggingface dataset:
Analisis Pengetahuan Mitigasi Bencana Banjir Bagi Mahasiswa Pendidikan Guru Sekolah Dasar FKIP Universitas Riau

This research is an analytical research that aims to describe the knowledge of flood disaster mitigation students of elementary school teacher education FKIP Riau University. This research is an analytical research using descriptive quantitative research. The subjects of this study were fifth semester students of Elementary School Teacher Education FKIP Riau University. knowledge test about flood disaster mitigation which consists of 12 essay questions. The data analysis technique used by the researcher in this analytical study is descriptive statistical calculation technique in the form of calculating knowledge test scores 1-4 with very good, good, sufficient and poor categories, then calculating the percentage value of each knowledge. The results of the overall student disaster mitigation knowledge assessment showed an average of 74.41% in the good category. The results of knowledge based on four indicators are each in the good category.
Training on the Development of Chemical Multiple Representations Integrated PowerPoint-iSpring Learning Media: a case of Chemistry Teachers in Padang

The training
Noise-Aware Statistical Inference with Differentially Private Synthetic Data

While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less. Existing work has shown that simply analysing DP synthetic data as if it were real does not produce valid inferences of population-level quantities. For example, confidence intervals become too narrow, which we demonstrate with a simple experiment. We tackle this problem by combining synthetic data analysis techniques from the field of multiple imputation (MI), and synthetic data generation using noise-aware (NA) Bayesian modeling into a pipeline NA+MI that allows computing accurate uncertainty estimates for population-level quantities from DP synthetic data. To implement NA+MI for discrete data generation using the values of marginal queries, we develop a novel noise-aware synthetic data generation algorithm NAPSU-MQ using the principle of maximum entropy. Our experiments demonstrate that the pipeline is able to produce accurate confidence intervals from DP synthetic data. The intervals become wider with tighter privacy to accurately capture the additional uncertainty stemming from DP noise.
Development of STEM-Based Flipbook Learning Media on the Bloodstream System Materials for Junior High School

This study aims to develop The STEM-based flipbook learning media on the Human Circulatory System material for junior high school students. This study uses research and development methods or Research and Development (R&amp;D) with the ADDIE research model and involves 7 validators to validate flipbook learning media. This research instrument uses a validation sheet consisting of a content validation sheet, media validation sheet, material validation sheet, language validation sheet and media and content validation sheet with analytical techniques using quantitative descriptive analysis. The results of this study are the development of STEM-based Flipbook learning media has been declared feasible with an average score of 81%. The criteria are very valid and get positive results from various validators. Based on the validation results, it shows that the learning media developed is suitable for use in learning, because the media has features (videos, quizzes, and STEM LKPD) that can be accessed online.
Optimization of Robot Trajectory Planning with Nature-Inspired and  Hybrid Quantum Algorithms

We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The core optimization module consists of a biased random-key genetic algorithm. Through a distinct separation of problem-independent and problem-dependent modules, we achieve an efficient problem representation, with a native encoding of constraints. We show that generalizations to alternative algorithmic paradigms such as simulated annealing are straightforward. We provide numerical benchmark results for industry-scale data sets. Our approach is found to consistently outperform greedy baseline results. To assess the capabilities of today's quantum hardware, we complement the classical approach with results obtained on quantum annealing hardware, using qbsolv on Amazon Braket. Finally, we show how the latter can be integrated into our larger pipeline, providing a quantum-ready hybrid solution to the problem.
Improving RENet by Introducing Modified Cross Attention for Few-Shot Classification

Few-shot classification is challenging since the goal is to classify unlabeled samples with very few labeled samples provided. It has been shown that cross attention helps generate more discriminative features for few-shot learning. This paper extends the idea and proposes two cross attention modules, namely the cross scaled attention (CSA) and the cross aligned attention (CAA). Specifically, CSA scales different feature maps to make them better matched, and CAA adopts the principal component analysis to further align features from different images. Experiments showed that both CSA and CAA achieve consistent improvements over state-of-the-art methods on four widely used few-shot classification benchmark datasets, miniImageNet, tieredImageNet, CIFAR-FS, and CUB-200-2011, while CSA is slightly faster and CAA achieves higher accuracies.
Pengaruh Motivasi Belajar Menggunakan Zoom dan Kecemasan Belajar Terhadap Prestasi Belajar Matematika Siswa Kelas X

The purpose of this study is that there are still many students who do not fully understand the use of zoom so that it affects learning motivation, some students still feel high learning anxiety so that it affects mathematics learning achievement, students who have low learning motivation affect learning achievement through learning anxiety. Furthermore, after the research implementation team conducted observations and interviews with several teachers at SMK N 2 Wonogiri, it was found that some students at the SMK still had difficulties in operating zoom and other similar media and the limited quota even though they received quota assistance but sometimes it was not sufficient. From this background, the research team wanted to investigate further about the effect of learning motivation using zoom and learning anxiety on mathematics learning achievement at SMK Negeri 2 Wonogiri. The method used is a quantitative approach, namely solving path analysis problems with a linear regression approach. The result of this research is that there is an effect of learning motivation using zoom on mathematics learning achievement. There is an influence of learning anxiety on mathematics learning achievement. There is an effect of learning motivation using zoom on mathematics learning achievement through learning anxiety. Keywords: learning motivation, zoom, learning anxiety, mathematics learning achievement
Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval

Multi-channel video-language retrieval require models to understand information from different channels (e.g. video$+$question, video$+$speech) to correctly link a video with a textual response or query. Fortunately, contrastive multimodal models are shown to be highly effective at aligning entities in images/videos and text, e.g., CLIP; text contrastive models are extensively studied recently for their strong ability of producing discriminative sentence embeddings, e.g., SimCSE. However, there is not a clear way to quickly adapt these two lines to multi-channel video-language retrieval with limited data and resources. In this paper, we identify a principled model design space with two axes: how to represent videos and how to fuse video and text information. Based on categorization of recent methods, we investigate the options of representing videos using continuous feature vectors or discrete text tokens; for the fusion method, we explore the use of a multimodal transformer or a pretrained contrastive text model. We extensively evaluate the four combinations on five video-language datasets. We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance, which can even outperform state-of-the-art on the iVQA and How2QA datasets without additional training on millions of video-text data. Further analysis shows that this is because representing videos as text tokens captures the key visual information and text tokens are naturally aligned with text models that are strong retrievers after the contrastive pretraining process. All the empirical analysis establishes a solid foundation for future research on affordable and upgradable multimodal intelligence.
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes

Interpretability is an essential building block for trustworthiness in reinforcement learning systems. However, interpretability might come at the cost of deteriorated performance, leading many researchers to build complex models. Our goal is to analyze the cost of interpretability. We show that in certain cases, one can achieve policy interpretability while maintaining its optimality. We focus on a classical problem from reinforcement learning: mazes with $k$ obstacles in $\mathbb{R}^d$. We prove the existence of a small decision tree with a linear function at each inner node and depth $O(\log k + 2^d)$ that represents an optimal policy. Note that for the interesting case of a constant $d$, we have $O(\log k)$ depth. Thus, in this setting, there is no accuracy-interpretability tradeoff. To prove this result, we use a new "compressing" technique that might be useful in additional settings.
The Basics of Quantum Computing

This paper tells about the basics of quantum computing. Quantum computing combines two scientific approaches from 20th century, technology and physics. Physics which is the conceptual basis of sound, electricity, and the structure of atoms, that allowed the computing revolution. However, on the algorithmic level it operates on “classical” logic. Quantum computing is a type of hardware and software system that uses quantum law to restore arithmetic logic at the algorithmic level. This guarantees dramatic speedups for explicit calculations such as optimization,
Investigating Gammatone Filterbank-Based i-Vectors for Speaker Verification Task

Most of the recent studies based on i-vector framework use conventional mel-frequency cepstral coefficient (MFCC) features. However, the main issue that is still faced with MFCC features is its sensitivity to noisy conditions. The verification performance degrades significantly in noisy conditions. In this paper, we replace the most commonly used MFCC features with new features known as gammatone cepstral coefficients (GFCCs) features. These features are built on gammatone filter bank. The speech data used in the experimentation are taken from TIMIT dataset. Our results show that combination of proposed GFCC features and conventional MFCC features in i-vector framework outperforms individual features not only for representing speaker specific information but also for making our verification system robust in noisy environment. Our experimental evaluations show that GFCC features outperforms the MFCC features with an improvement in equal error rate (EER) by 1% in matched noise conditions and 0.45% in mismatched noise conditions.
Clustering Data Penjualan Produk Makanan pada Toko Toserba Yogya Siliwangi dengan Menggunakan Metode K-Means

Product availability is one of the important factors to increase sales and maintain customer satisfaction in meeting their needs. With this, the company needs to analyze sales data, both for the best-selling products or those that are not selling well from sales reports every month, especially for food products. Of course, this is not easy, especially for a large enough retailer such as the Yogya Siliwangi Toserba which has thousands of product items and thousands of sales data every month. The above problems can be solved by grouping the data using the k-means clustering algorithm on rapidminer with variables taken by the name of goods, incoming goods, outgoing goods and stock. The goal is to maximize sales and maintain product stock availability to meet the diverse needs of consumers. From the calculation of the k-means algorithm using the rapidminer application, the results obtained are in the form of three clusters, cluster_1 3 items, cluster_2 13 items and cluster_0 454 items with Devies Bouldin results being 0.478.
Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering

Most natural &lt;span&gt;world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median to identify overlapping clusters in the presence of outliers. This new method aims to determine the insensitivity of the OCKMEx algorithm in locating data points that overlap even with outliers. An experimental evaluation of the algorithm was conducted wherein synthetic datasets served as a data source, and the F1 measure criterion was applied to assess the OCKMEx algorithm performance. Results indicate that the OCKMEx algorithm implementing the use of k-median performed a higher accuracy rate of 100% in identifying data points that overlap even with outliers compared to the existing k-means algorithm. The algorithm exhibited a promising performance in identifying overlapping clusters and was resistant to outliers.&lt;/span&gt;
Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts

Recent works suggest that transformer models are capable of multi-tasking on diverse NLP tasks and adapting to new tasks efficiently. However, the potential of these multi-task models may be limited as they use the same set of parameters for all tasks. In contrast, humans tackle tasks in a more flexible way, by making proper presumptions on what skills and knowledge are relevant and executing only the necessary computations. Inspired by this, we propose to use task-level mixture-of-expert models, which has a collection of transformer layers (i.e., experts) and a router component that chooses from these experts dynamically and flexibly. We find that these models help improve the average performance gain (ARG) metric by 2.6% when adapting to unseen tasks in the few-shot setting and by 5.6% in the zero-shot generalization setting. Further, we show that the learned routing decisions partly rediscover human categorization of NLP tasks -- certain experts are strongly associated with extractive tasks, some with classification tasks, and some with tasks requiring world knowledge.
A comparative study of Naive Bayes Classifiers with improved technique on Text Classification

&lt;p&gt;Experiment was carried out on imbalanced data having positive and negative labels as 0 and 1. These datasets after training were tested on Gaussian Naive Bayes, Bernoulli Naive Bayes and Multinomial Naive Bayes with improved technique using tf-idf and ngram. The results obtained were then compared with old model result that make use of BagofWords. On testing it is found that there is a 2-3% improvement in the model's performance.&lt;/p&gt;
COMPARATIVE EXAMINATION OF CONVENTIONAL DIRECT SPUTUM AND INDIRECT SEDIMENTATION ON CYTOSPIN TUBERCULOSIS PATIENT SPUTUM SAMPLES

Microscopic examination of sputum using the Ziehl-Neelsen stain is the gold standard for Tuberculosis (TB), but it must be performed by experts with special skills. The purpose of this study is to accelerate the determination of the microscopic results using the Ziehl-Neelsen stain. This research is an experimental method in which the test sample is treated and the sputum sample is controlled with up to 25 samples. The method development is very important to increase the sensitivity and specificity of the results of TB examination using microscopes. This observation shows that the indirect cytospin method has a narrower reading range on a circle with a diameter of only 7 mm, making it easier for a bacterial count compared to the traditional direct method with a size of 2 x 3 cm oval shape. The results of the microscopic examination were 21 positive specimens and 4 negative specimens. Mycobacterium tuberculosis with Ziehl-Neelsen staining gave the same results with a sensitivity and specificity of 100%.
Modeling Air Temperature in Forested Areas using Machine Learning

Air Temperature is a fundamental measure of the Earth’s climate but is only measured at fixed locations. Land surface temperature can be measured widely using satellites. To estimate air temperature (Ta) from the surface temperature (Ts) measured on the forested slopes of Kilimanjaro, four models with unique sets of inputs were tested using five machine learning algorithms. The RMSE for each model was compared with a benchmark model. Models and algorithms were ranked according to their RMSE (Root Mean Square Error) The models and algorithms reliability and consistency ranking were calculated. The best model and algorithm were determined. Novel models results were compared with the benchmark model. All models outperformed the benchmark model in the consistency ranking while three out of four models outperformed the benchmark model in the reliability ranking. Thus machine learning improves the estimation of air temperature in this forested environment.
Photography Workshop for School Dropout Youth in Nagari Batu Bulek, North Lintau Buo District, Tanah Datar Regency

Tourism in the era of the COVID-19 pandemic could be suspended if entrepreneurs, institutions, ormanagers do not immediately switch to using digital technology. Promotion of tourism, offers,destinations, hotels, culinary, souvenirs, and everything related to tourism requires digital technology.Through this technology, photos or videos related to tourism promotion can be presented. Photos orvideos that are made professionally and presented briefly, concisely, and precisely, are the mainattraction for tourists. Therefore, the presence of a professional photographer is needed. Puncak Pato isthe second most favored destination in Tanah Datar Regency, after the Pagaruyung Traditional House.This tourist location is located in Nagari Batu Bulek, North Lintau Buo District, Tanah Datar Regency,West Sumatra. In this village there is a group of out of school youth. They don't have permanent jobs.This youth group has a strong interest in photography, but does not yet have sufficient competence.Through the Community Partnership Program, the Community Service Group of the Faculty ofMathematics and Natural Sciences UNP provides workshops to improve their competence inphotography. The results of the pre-test and post-test for the knowledge and skills of the participants,which consisted of 7 people, showed that there had been an increase in the competence of theparticipants. It is hoped that they can become professional photographers who are the spearhead of thepromotion of Puncak Pato and other tours in Tanah Datar Regency throughout the world.
Features of balanced online and offline teaching of mathematics to students with disabilities

In modern conditions of the accelerated development of inclusive education in higher education, there is a need for a continuous search for appropriate and flexible solutions in the choice of technologies, methods and teaching aids. A detailed study, development and implementation of specific technologies are required to ensure the active and effective inclusion of students with disabilities in the educational process. In this regard, the main problem is the teachers choice of didactic methods, tools and technologies that allow organizing the learning process with the proper degree of effectiveness for all students, including students with disabilities. Modern inclusive education involves information barriers elimination for knowledge development, which is ensured by the active inclusion of distance learning technologies in the educational process. At the same time, a complete transition to a distance learning format negatively affects the process of social adaptation of people with disabilities, so a reasonable combination of online and offline forms of education is the most acceptable. This paper analyzes the problem of building the most effective model for teaching people with disabilities, based on the interaction of traditional and distance learning technologies. An example of solving this problem is considered based on the experience of teaching mathematical courses at Povolzhskiy State University of Telecommunications and Informatics and Samara State University of Social Sciences and Education.
A Study of Pre-editing Methods at the Lexical Level in the Process of Machine Translation

As the demand for translation increases, the role of machine translation in improving the efficiency of translation is increasingly prominent, but due to its inherent limitations, the translation quality of machine translation is not guaranteed. Pre-editing is one of the ways to enhance the quality of machine translated texts. With Google online translation as a tool, the effect of pre-editing on the quality of machine translation is studied by adopting the Bilingual Evaluation Understudy (BLEU) approach. The pre-editing methods of additions, omissions, replacing and terminology preprocessing based on the lexical level are proposed. The results show these methods can play a role in improving the quality of English-to-Chinese machine translations. In most cases, pre-editing at the lexical level is sufficient to generate high-quality machine translation output, but the effectiveness of pre-editing can be further improved when perspective-shifting is involved or when the target language involves contextually consistent verb or person usage
OPPORTUNITIES FOR USE OF DIGITAL INNOVATIONS IN EDUCATION

The paper reflects the results of research on improving the effectiveness of innovative technologies in education, as well as on the possible prospects associated with digitalization.The regulatory and legal support for the development of digital innovations in various sectors of the economy and the educational sphere is analyzed.Based on the experimental work carried out, the experience of introducing digital innovations in education, a number of measures for the successful implementation of educational programs are proposed.The possibilities of using modern software based on existing learning management systems are identified.
INTRODUCTION OF INFORMATION TECHNOLOGIES IN THE EDUCATIONAL PROCESS

This article presents how information technology processes are improved and adapted to their inherent reality, as well as used in the learning process. The article discusses the role of computer science and computer technology in the education system. It is revealed that the transformation of the educational system is accompanied by fundamental problems: the rapid obsolescence of IT products that are subject to constant updating, and insufficient economic resources.