Reviewerly/exHarmony huggingface dataset:
ANALISIS HIDROLOGI RANCANGAN MENGGUNAKAN METODE RASIONAL PADA SALURAN DRAINASE DI KELURAHAN SUMERTA KELOD KOTA DENPASAR

Tukad Kelandis is a tributary of the Tukad Ayung Watershed which is located downstream. In 2021 floods occurred in Tukad Kelandis resulting in one of the places affected by flooding, namely the Art Center area. So it is necessary to do research on the analysis of peak flood discharge on the Tukad Kelandis river using rational methods. Maximum runoff occurs when the rainfall is during the same time as the catchment area concentration time in the rational method. Based on the research results, the amount of planned flood discharge in the Tukad Kelandis watershed was obtained using the rational method for return periods of 2, 5, 10, 20, and 25 years, namely: Q2 Year = 273,890 m3/s, Q5 Year = 337,372 m3/s, Q10 Year = 373,194 m3/ s, Q20 Year = 399,610 m3/ s, Q25 Year = 413,406 m3/ s.
PEMODELAN DAMPAK PERUBAHAN LAHAN TERHADAP DEBIT BANJIR PADA DAS MENINTING DENGAN HEC-HMS

Changes in land use in a developing area are difficult to avoid, including in watersheds which result in increased flood discharge. The Meninting River is one of the important rivers on the Lombok island which is experiencing relatively rapid land change. The purpose of this study was to determine the effect of land use change on flood discharge in the Meninting Watershed. The data needed is in the form of watershed maps, rainfall data, land use data and soil types. Land use maps were obtained from the results of Google Earth analysis for 2012, 2017 and 2022. The simulation uses daily rainfall data. The results of the design rain analysis are distributed into hourly rain using the Mononobe method. Data on soil types and land use are used to calculate the CN (curve number) value. Flood analysis using HEC-HMS software.The results of research using various return periods, namely 2 year, 5 year, 10 year, 25 year, 50 and 100 year return period, show an increase in CN values by 0.26% in 2017 and 1.22% in 2022. Changes in land use the land causes an average increase in flood discharge by 0.41% in 2017 and 1.19% in 2022.
ENGLISH LEARNING STRATEGIES USED BY THE STUDENTS IN ENGLISH EDUCATION DEPARTMENT IN SOLVING THEIR LEARNING DIFFICULTIES

This study focuses on the learning strategies used by students majoring in English Education in Overcoming Learning at STKIP Paracendekia NW Sumbawa. The purpose of this study was to determine the strategies applied and the difficulties faced by fourth semester students majoring in English education. This research is qualitative research. Researchers used two instruments to obtain the required data, namely: observation and interviews. Based on the data obtained from the second instrument used, the following findings were obtained. First, in the process of learning English, students with high predicate also experience learning difficulties (the most dominant learning difficulty faced when learning English were the limitation of vocabulary, differences in speakers’ accent, grammar, and pronunciation respectively). Second, successful students use not only single learning strategies but they use mixed learning strategies including memory, cognitive, compensatory, metacognitive, affective and social strategies.
3D Visual Simulation of Hydraulic Engineering Based on Adaptive Genetic Algorithm

The application of visualization technology in water conservancy projects is in the ascendant. It is necessary to analyze and study the key technologies and difficulties involved in 3D visualization simulation of water conservancy projects in combination with relevant research work, and provide corresponding solutions. Most water conservancy projects are large and complex in recent years, the rapid economic development has led to increased energy consumption, and the demand for the development of clean and renewable resources such as water is growing. 3D visual engineering design has entered the practical stage in the advanced industry, but its development in the hydraulic field lags behind. In order to solve the problem that traditional genetic algorithms are easy to fall into local optimal solutions and have unstable performance, an adaptive genetic algorithm (AGA) based on co evolution is proposed. On the basis of the two-layer framework model of co evolution, an adaptive mutation strategy is introduced to improve the local evolution operation in the co evolution genetic algorithm and strengthen the local search in the upper layer. That is, the global mutation operator and the local mutation operator work together to improve the distribution characteristics of the population, strengthen the global convergence ability of the algorithm. The genetic algorithm of the probability model is used to optimize four famous test functions. The experimental results show that the algorithm is not easy to fall into the local extreme value. The convergence speed is fast and the implementation is simple.
Virtual mentoring in agricultural education: Describing digital literacy, technology self-efficacy, and attitudes toward technology of secondary agricultural educators

Digital literacy, technology self-efficacy, and attitude toward technology play an impactful role in the life of teachers. Proficiency in digital literacy and technology self-efficacy, along with a positive attitude toward technology, can predict and shape an educator's capacity to implement best practices for teaching and learning with technology. This study examined the effects of participation in a virtual mentoring program on the digital literacy, technology self-efficacy, and attitude toward technology of secondary agriculture teachers. We found secondary agriculture teachers who had participated in a virtual mentoring program exhibited higher overall digital literacy levels and technology self-efficacy levels than those secondary agriculture teachers who had not participated in a virtual mentoring program. Pragmatic implications include a defined opportunity for agricultural education leaders to develop appropriate and beneficial tools and materials to assist in-service agriculture teachers in developing digital literacy skills, technology self-efficacy skills, and a positive attitude toward technology. Recommendations for future research include examining different means of establishing virtual mentoring relationships between secondary agriculture teachers and exploring virtual professional development opportunities designed to help educators develop digital literacy, technology self-efficacy, and a positive attitude toward technology.
Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms

Summary Quantum computing is a rapidly growing technology field with increasingly useful applications across industry and research. This new paradigm of computing has the potential to solve classically intractable problems by exploiting an exponentially increasing computational space. This allows quantum algorithms to dramatically reduce the runtime for solving computationally resource-intensive problems. Quantum Neural Networks (QNNs) [ 1 - 4 ] present a promising opportunity for overcoming the scaling problem in classical machine learning. However, training QNNs requires significant circuit evaluations, making them a resource-intensive use case for quantum processing units (QPUs). This study compares software development kits (SDKs) and hardware platforms to determine the fastest and most cost-efficient combination for developing novel quantum algorithms and uses QNNs as a general benchmark. The aim is to find the optimal balance of runtime, cost, and accuracy for executing quantum circuits.
Quantum Computing with Neutral Atoms with Applications in Energy

Summary This talk explores the potential of quantum computing and the current market status. It focuses on PASQAL’s technology using neutral atoms for scalable quantum computing. The main categories of algorithms designed for neutral atom quantum computers and their real-world applications are summarized. The energy industry has numerous quantum applications, including energy resource management, grid optimization, complex system optimization, simulation, and battery design. Three disruptive examples are highlighted. Integration of quantum hardware with HPC centers is crucial. The talk addresses challenges, the expected maturity of quantum computing, and the integration challenges with HPC centers. It prompts us to consider the future of HPC centers in the era of quantum computing.
Protocols Using Single Quantum Systems

Currently, theoretical and experimental work in the field of quantum cryptography is being carried out in many research centers around the world. Over the past two decades since the start of active research, many quantum cryptographic protocols have been proposed, a significant number of laboratory experiments have been performed on their practical implementation, important work has been done to prove the resistance of the proposed protocols to certain types of attacks, and the first commercial quantum key distribution systems have been developed. However, there are still many tasks and problems in the field of quantum cryptography, the solution of which is of great scientific and technical importance. So, in particular, such a promising direction as quantum protocols for direct secure communication requires further development, allowing, under certain conditions, to transfer secret information without encryption, both between two and between a large number of users. Currently, such protocols have been developed using two-and three-particle entangled states, but there are almost no similar protocols with entangled states of a larger number of qubits that do not require large quantum memory for their implementation and, at the same time, have a large information capacity. Therefore, the development of new quantum direct secure communication protocols with increased information capacity, the analysis of their resistance to attacks, as well as the determination of the best protocols according to the criteria of resistance and information capacity.
Institutional quality-led Technological Innovation in Lower-income nations: Does Trade and Education matter

The primary objective of this study is to comprehensively examine the underlying factors that contribute to the promotion of technological advancement within these nations, with the ultimate aim of offering well-informed and efficacious policy recommendations. The present study utilizes a robust methodology, incorporating a comprehensive dataset and employing rigorous statistical analysis techniques. The study's results demonstrate a noteworthy and statistically significant correlation between institutional quality and technological innovation. This suggests that nations with more robust institutions foster an environment conducive to developing and advancing innovative practices. The present study is underpinned by a comprehensive review of existing literature, which substantiates and reinforces the significance of institutions in fostering and facilitating innovation and entrepreneurship. Moreover, the present study reveals a significant positive correlation between trade openness and technological innovation, underscoring the facilitative impact of domestic and international trade on the innovation process. Furthermore, this study elucidates the constructive impact of education on technological innovation, underscoring the significance of easily attainable and high-quality education in nurturing innovation. Based on the empirical evidence presented in this study, it is recommended that policymakers accord high priority to implementing institutional reforms, facilitating trade integration, and allocating resources towards education to effectively stimulate and nurture technological advancements within lower-income nations. Nevertheless, it is imperative to acknowledge the necessity of contemplating alternative explanations and contextual factors that might influence the interplay between these variables. This study offers significant insights that can greatly benefit policymakers and practitioners interested in fostering innovation-driven development in lower-income countries.
BAHAN AJAR MENYAJIKAN TEKS PERSUASIF DENGAN MEDIA GAMBAR BERSERI UNTUK SISWA KELAS VIII SMP/MTS

This research is motivated by the problem of teaching materials used by teachers and students. So far, persuasive text teaching materials used in class VIII SMP/MTs are still very limited. The learning process with a lack of appropriate techniques and media is one of the factors in learning persuasive texts that are less attractive and difficult. The formulation of the problem in this study is formulated as follows. (1) How do the teaching materials present persuasive texts with serial picture media for Grade VIII students of SMP/MTs?; (2) Are teaching materials presenting persuasive texts using serialized images media appropriate to be used as alternative teaching materials for class VIII students of SMP/MTs?; (3) How is the student's response to teaching materials presenting persuasive texts with serial picture media for Grade VIII students of SMP/MTs?&#x0D; The purpose of this research is to know and describe: (1). teaching materials presenting persuasive texts with serial picture media for class VIII students of SMP/MTs; (2) the feasibility of teaching materials in presenting persuasive texts with serial picture media for class VIII students of SMP/MTs; (3) student responses to teaching materials presenting persuasive texts with serial picture media for class VIII students of SMP/MTs.&#x0D; The method used in this research is descriptive method with a qualitative approach. The data sources for this study were respondents, namely two Indonesian language teachers as validators and five students of class VIII SMP. Meanwhile, the data in this study were from previous research, reference books, and similar research journals. The techniques used are interview techniques and questionnaires or questionnaires.&#x0D; Based on the results of the study it can be concluded as follows. (1) The teaching materials are arranged in a systematic and structured manner in accordance with KD in the 2013 Curriculum. The teaching materials present persuasive texts with serial picture media including instructions for using modules, concept maps, core competencies, student worksheets&#x0D; &#x0D; and answer key sheets. Teaching materials presenting persuasive texts have two learning activities, namely examining the structure and language rules of persuasive texts and presenting persuasive texts. (2) The results of the combined validation between material experts and media experts obtained an average value of 86.50%. This value indicates that teaching materials presenting persuasive text with serial image media are very valid and appropriate to use. (3) The results of student responses or responses as a whole to teaching materials, obtained an average value of 87.5. This value indicates that the five students responded very well to teaching materials presenting persuasive texts using serial picture media.
ANALISIS KESALAHAN EJAAN PADA BERITA POLITIK EDISI APRIL 2023 DALAM MEDIA MASSA DARING (DETIK.COM)

This study analyzes and describes spelling errors in political news in the April 2023 edition of the Detik.com newspaper. The data in this study are spelling errors in the form of capital letters errors, word writing errors, italics errors, symbol writing errors. numbers, and punctuation errors. The objectives of this research include, among others, (1) knowing the types of spelling errors that exist in political news in the April 2023 edition of the Detik.com mass media and (2) analyzing and describing the forms of errors in the April 2023 edition of the Detik.com mass media. This research is a descriptive qualitative research with the subject of finding, analyzing, and describing data in the form of spelling errors. According to (Sugiyono, 2022) says that, "The qualitative research method is a research method based on the philosophy of postpositivism, used to research the conditions of natural objects, (as opposed to experiments) where the researcher is a key instrument, data collection techniques are carried out by triangulation (combined), data analysis is inductive or qualitative, and the results of qualitative research emphasize meaning rather than generalization. The results of this study are as follows. (1) Errors in writing capital letters with lots of data, ten errors in writing capital letters, (2) Errors in writing words with lots of error marks, totaling twelve errors, (3) Errors in writing italics with a total of twenty-two errors, (4 ) Errors in writing number symbols with lots of data errors, namely four writing errors, and (5) Errors writing punctuation marks with lots of data errors totaling nine writing errors.
A Comparison of Deep Learning for Software Features Extraction in Forensic Online News

Software features of forensics are functional components in software. Software feature extraction is performed to detect software features in documents in the form of online news with a forensic category. This study is conducted to find a suitable deep learning model for software feature extraction. This study uses a deep learning approach and CRF layers to perform software feature extraction. The deep learning methods used are BiLSTM-CRF, BiGRU-CRF, and LSTMCRF. The learning process uses Word Embedding models such as Glove, Word2Vec, and Fasttext. The dataset is collected through scraping from online news with the forensic category. The news was tokenized by word level into datasets and annotated. Tests compare deep learning methods that do not use the word embedding model and those that use word embedding. The experimental results show an increase of 2% - 7% in performance metrics. Combining the Fasttext and BiLSTM-CRF word embedding models results in the best performance, with a precision of 94.03%, a recall of 95.60%, an F1-measure of 93.66%, and an accuracy of 98.99%.
Artificial Intelligence In Language Teaching And Learning

Due to its capacity to simulate human intelligence processes that are managed by machines, particularly computer systems, artificial intelligence (AI) seems to have numerous applications nowadays. From this perspective, this article presents a literature review of the applications of AI in the teaching and learning of languages. A search of articles was carried out in the Google Scholar database. The method used to review the articles selected is the qualitative research method, specifically content analysis. The study's conclusions show, due to its many advantages, AI is essential in language teaching and learning, being a meaningful tool for both teachers and students.
Research and Improvement of a Plagiarism Detection Methods Using Blockchain Technolog

This paper proposes unified algorithm for plagiarism detection using Blockchain technology, machine learning for text processing and SimHash for identifying near-duplicate documents at scale. Blockchain is a decentralized database that allows you to conduct transactions based on network consensus. This may affect the way plagiarism is checked, as Blockchain will act as a transparent system of protection against unauthorized access or a system of social contracts. Higher education institutions can use blockchain technology for various academic activities, such as administering online courses. Almost every field of teaching and learning is a potential beneficiary of Blockchain technology. In general, the blockchain can be used to build a balance sheet to measure the learning process and outcomes. It is a democratized proof of values for all. Most Blockchain applications are currently at the level of Blockchain 1.0 and Blockchain 2.0, which allows us to predict its development. Also, the blockchain perfectly solves the problem of fraud in general due to its next abilities - decentralization, consistency and the ability to act as a global permitting system. In addition, besides using blockchain technology, multi-language support can provide strong benefits to overall system. Also this paper show how current unified algorithm beats the score of TF-IDF algorithm overall by 7%.
Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings

Base45 encodes pairs of octets using 3 characters from an alphabet of 45 printable symbols. Previous works showed the ability to hide payload data into encoded strings by exploiting the unused configurations of the Base45 encoding. In this paper, we present Hide45, an algorithm for hiding data into Base45 encoded strings that optimizes the embedding capacity, according to the frequency distribution of the input data. Experimental tests show that an optimal assignment of bit configurations to the most frequent pairs of octets allows to reach a payload capacity very close to the theoretical capacity of the method, improving over a baseline assignment by up to 53%.
Distributed Active Partial Label Learning

Active learning (AL) trains a high-precision predictor model from small numbers of labeled data by iteratively annotating the most valuable data sample from an unlabeled data pool with a class label throughout the learning process. However, most current AL methods start with the premise that the labels queried at AL rounds must be free of ambiguity, which may be unrealistic in some real-world applications where only a set of candidate labels can be obtained for selected data. Besides, most of the existing AL algorithms only consider the case of centralized processing, which necessitates gathering together all the unlabeled data in one fusion center for selection. Considering that data are collected/stored at different nodes over a network in many real-world scenarios, distributed processing is chosen here. In this paper, the issue of distributed classification of partially labeled (PL) data obtained by a fully decentralized AL method is focused on, and a distributed active partial label learning (dAPLL) algorithm is proposed. Our proposed algorithm is composed of a fully decentralized sample selection strategy and a distributed partial label learning (PLL) algorithm. During the sample selection process, both the uncertainty and representativeness of the data are measured based on the global cluster centers obtained by a distributed clustering method, and the valuable samples are chosen in turn. Meanwhile, using the disambiguation-free strategy, a series of binary classification problems can be constructed, and the corresponding cost-sensitive classifiers can be cooperatively trained in a distributed manner. The experiment results conducted on several datasets demonstrate that the performance of the dAPLL algorithm is comparable to that of the corresponding centralized method and is superior to the existing active PLL (APLL) method in different parameter configurations. Besides, our proposed algorithm outperforms several current PLL methods using the random selection strategy, especially when only small amounts of data are selected to be assigned with the candidate labels.